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Title: CHANGES IN RUNNING AND MULTIPLE OBJECT TRACKING PERFORMANCE DURING A 90-MINUTE INTERMITTENT SOCCER PERFORMANCE TEST (iSPT). A PILOT STUDY.
Creator: Girts, Ryan, Wells, Adam, Stout, Jeffrey, Fukuda, David, Hoffman, Jay, University of Central Florida
Abstract / Description: Multiple object tracking (MOT) is a cognitive process that involves the active processing of dynamic visual information. In athletes, MOT speed is critical for maintaining spatial awareness of teammates, opponents, and the ball while moving at high velocities during a match. Understanding how MOT speed changes throughout the course of a competitive game may enhance strategies for maintaining optimal player performance. The objective of this study was to examine changes in MOT speed and...
Show moreMultiple object tracking (MOT) is a cognitive process that involves the active processing of dynamic visual information. In athletes, MOT speed is critical for maintaining spatial awareness of teammates, opponents, and the ball while moving at high velocities during a match. Understanding how MOT speed changes throughout the course of a competitive game may enhance strategies for maintaining optimal player performance. The objective of this study was to examine changes in MOT speed and running performance during a 90-minute intermittent soccer performance test (iSPT). A secondary purpose was to examine the relationship between aerobic capacity and changes in MOT speed.Seven competitive female soccer players age: 20.4 (&)#177; 1.8 y, height: 166.7 (&)#177; 3.2 cm, weight: 62.4 (&)#177; 4.0 kg, VO2max: 45.8 (&)#177; 4.6 ml/kg/min-1) completed an intermittent soccer performance test (iSPT) on a Curve(TM) non-motorized treadmill (cNMT). The iSPT was divided into two 45-minute halves with a 15-minute halftime [HT] interval, and consisted of six individualized velocity zones. Velocity zones were consistent with previous time motion analyses of competitive soccer matches and based upon individual peak sprint speeds (PSS) as follows: standing (0% PSS, 17.8% of iSPT), walking (20% PSS, 36.4% of iSPT), jogging (35% PSS, 24.0% of iSPT), running (50% PSS, 11.6% of iSPT), fast running (60% PSS, 3.6% of iSPT), and sprinting (80% PSS, 6.7% of iSPT). Stand, walk, jog and run zones were combined to create a low-speed zone (LS). Fast run and sprint zones were combined to create a high-speed zone (HS). MOT speed was assessed at baseline (0 min.) and three times during each half of the iSPT. Dependent t-tests and Pearson correlation coefficients were utilized to analyze the data. Across 15-minute time blocks, significant decreases in distance covered and average speed were noted for jogging, sprinting, low-speed running, high-speed running, and total distance (p's (<) 0.05). Players covered significantly less total distance during the second half compared to the first (p = 0.025). Additionally, significant decreases in distance covered and average speed were observed during the second half for the sprint and HS zones (p's ? 0.008). No significant main effect was noted for MOT speed across 15-minute time blocks. A trend towards a decrease in MOT speed was observed between halves (p = 0.056). A significant correlation was observed between the change in MOT speed and VO2max (r = 0.888, p = 0.007). The fatigue associated with 90 minutes of soccer specific running negatively influenced running performance during the second half. However, increased aerobic capacity appears to be associated with an attenuation of cognitive decline during 90-minutes of soccer specific running. Results of this study indicate the importance of aerobic capacity on maintaining spatial awareness during a match.
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Date Issued: 2018
Identifier: CFE0007183, ucf:52290
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007183

Title: REACTIVE CONTROL OF AUTONOMOUS DYNAMICAL SYSTEMS.
Creator: Chunyu, Jiangmin, Qu, Zhihua, University of Central Florida
Abstract / Description: This thesis mainly consists of five independent papers concerning the reactive control design of autonomous mobile robots in the context of target tracking and cooperative formation keeping with obstacle avoidance in the static/dynamic environment. Technical contents of this thesis are divided into three parts. The first part consists of the first two papers, which consider the target-tracking and obstacle avoidance in the static environment. Especially, in the static environment, a...
Show moreThis thesis mainly consists of five independent papers concerning the reactive control design of autonomous mobile robots in the context of target tracking and cooperative formation keeping with obstacle avoidance in the static/dynamic environment. Technical contents of this thesis are divided into three parts. The first part consists of the first two papers, which consider the target-tracking and obstacle avoidance in the static environment. Especially, in the static environment, a fundamental issue of reactive control design is the local minima problem (LMP) inherent in the potential field methods (PFMs). Through introducing a state-dependent planned goal, the first paper proposes a switching control strategy to tackle this problem. The control law for the planned goal is presented. When trapped into local minima, the robot can escape from local minima by following the planned goal. The proposed control law also takes into account the presence of possible saturation constraints. In addition, a time-varying continuous control law is proposed in the second paper to tackle this problem. Challenges of finding continuous control solutions of LMP are discussed and explicit design strategies are then proposed. The second part of this thesis deals with target-tracking and obstacle avoidance in the dynamic environment. In the third paper, a reactive control design is presented for Omni-directional mobile robots with limited sensor range to track targets while avoiding static and moving obstacles in a dynamically evolving environment. Towards this end, a multi-objective control problem is formulated and control is synthesized by generating a potential field force for each objective and combining them through analysis and design. Different from standard potential field methods, the composite potential field described in this paper is time-varying and planned to account for moving obstacles and vehicle motion. In order to accommodate a larger class of mobile robots, the fourth paper proposes a reactive control design for unicycle-type mobile robots. With the relative motion among the mobile robot, targets, and obstacles being formulated in polar coordinates, kinematic control laws achieving target-tracking and obstacle avoidance are synthesized using Lyapunov based technique, and more importantly, the proposed control laws also take into account possible kinematic control saturation constraints. The third part of this thesis investigates the cooperative formation control with collision avoidance. In the fifth paper, firstly, the target tracking and collision avoidance problem for a single agent is studied. Instead of directly extending the single agent controls to the multi-agents case, the single agent controls are incorporated with an existing cooperative control design. The proposed decentralized control is reactive, considers the formation feedback and changes in the communication networks. The proposed control is based on a potential field method; its inherent oscillation problem is also studied to improve group transient performance.
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Date Issued: 2010
Identifier: CFE0003421, ucf:48384
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003421

Title: CONTROL OF NONHOLONOMIC SYSTEMS.
Creator: Yuan, Hongliang, Qu, Zhihua, University of Central Florida
Abstract / Description: Many real-world electrical and mechanical systems have velocity-dependent constraints in their dynamic models. For example, car-like robots, unmanned aerial vehicles, autonomous underwater vehicles and hopping robots, etc. Most of these systems can be transformed into a chained form, which is considered as a canonical form of these nonholonomic systems. Hence, study of chained systems ensure their wide applicability. This thesis studied the problem of continuous feed-back control of the...
Show moreMany real-world electrical and mechanical systems have velocity-dependent constraints in their dynamic models. For example, car-like robots, unmanned aerial vehicles, autonomous underwater vehicles and hopping robots, etc. Most of these systems can be transformed into a chained form, which is considered as a canonical form of these nonholonomic systems. Hence, study of chained systems ensure their wide applicability. This thesis studied the problem of continuous feed-back control of the chained systems while pursuing inverse optimality and exponential convergence rates, as well as the feed-back stabilization problem under input saturation constraints. These studies are based on global singularity-free state transformations and controls are synthesized from resulting linear systems. Then, the application of optimal motion planning and dynamic tracking control of nonholonomic autonomous underwater vehicles is considered. The obtained trajectories satisfy the boundary conditions and the vehicles' kinematic model, hence it is smooth and feasible. A collision avoidance criteria is set up to handle the dynamic environments. The resulting controls are in closed forms and suitable for real-time implementations. Further, dynamic tracking controls are developed through the Lyapunov second method and back-stepping technique based on a NPS AUV II model. In what follows, the application of cooperative surveillance and formation control of a group of nonholonomic robots is investigated. A designing scheme is proposed to achieves a rigid formation along a circular trajectory or any arbitrary trajectories. The controllers are decentralized and are able to avoid internal and external collisions. Computer simulations are provided to verify the effectiveness of these designs.
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Date Issued: 2009
Identifier: CFE0002683, ucf:48220
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002683

Title: Effects of symbol type on naming and identification of graphic symbols by typically developing three, four, five and six-year olds children.
Creator: Resnick, Pamela, Kent-Walsh, Jennifer, Schwartz, Jamie, Zraick, Richard, Bai, Haiyan, University of Central Florida
Abstract / Description: Speech-language pathologists and educators face unique challenges in assessing the language skills of children with complex communication needs due to the wide array of impairments with which these individuals present. For example, most receptive language assessment tools require that children either point to or label line drawings to determine whether or not they comprehend the depicted concepts; task demands such as these preclude administering such assessment tools with children who are...
Show moreSpeech-language pathologists and educators face unique challenges in assessing the language skills of children with complex communication needs due to the wide array of impairments with which these individuals present. For example, most receptive language assessment tools require that children either point to or label line drawings to determine whether or not they comprehend the depicted concepts; task demands such as these preclude administering such assessment tools with children who are unable to physically point to or verbally label presented stimuli. In light of these challenges, the use of eye tracking technologies has become particularly appealing since this alternate response mode reduces the behavioral demands associated with standardized assessment procedures. Another challenge clinicians and educators face as they strive to ensure accurate receptive language assessment results with children who have complex communication needs is the type of stimuli utilized in such assessments. When individuals with cognitive delays are presented with stimuli that may not be comprehensible to them, there is a risk of under-estimating language comprehension abilities (Emerson, 2003). Given the documented challenges that individuals with disabilities often have in identifying constructs depicted by the types of line drawings typically included in receptive language assessment tools (e.g., Mirenda (&) Locke, 1989; Mizuko, 1987), there is a critical need to include recognizable stimuli in assessment tools in order to determine this population's true receptive language capabilities. Beyond this potential to improve the validity of receptive language assessments, improvement in assessment practices such as these also have potential positive implications for effective AAC technology selection and AAC treatment planning.The current investigation examined the effect of symbol type (color photograph symbols1 vs. SymbolStix(&)copy;2 color line drawing symbols) on identification and naming of graphic symbols for nouns, verbs and adjectives in typically developing three, four, five and six-year old children. A quasi-experimental design was employed, with counterbalance for experimental stimuli (color photograph symbols1 vs. SymbolStix(&)copy;2 symbols) and task (identification task vs. naming task). Eighty-nine participants completed the identification and naming tasks with both examined symbol types (color photograph symbols1 vs. SymbolStix(&)copy;2 symbols) on two different days. Multivariate Analysis of Variance (MANOVA) was used to examine the effects of symbol type on both accuracy and rate of identification, and on accuracy of naming. Bivariate correlation was completed to determine the relationship between participants' touch and eye identification rates, and to determine the relationship between identification accuracy and eye rate. Mean scores revealed that all participants achieved higher accuracy for the identification and naming tasks with color photograph symbols1, and that participants evidenced faster touch and eye identification rates for the color photograph symbol1 condition. These findings suggest that color photograph symbols1 are more transparent and thus more easily identifiable. Therefore, potential future assessment modifications include the incorporation of color photograph symbols1 as stimuli and eye gaze as a selection option within AAC assessment tools. Overall, results of this study have the potential to change the way speech-language pathologists and educators assess the receptive language skills of children with complex communication needs to yield more accurate assessment results.
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Date Issued: 2017
Identifier: CFE0006909, ucf:51701
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006909

Title: Transient Multi-scale Computational Fluid Dynamics (CFD) Model for Thrombus Tracking in an Assit Device Vascular Bed.
Creator: Osorio, Ruben, Kassab, Alain, Divo, Eduardo, Ilie, Marcel, University of Central Florida
Abstract / Description: Heart failure occurs when the heart is not capable to pump blood at a sufficient rate to meet the demands of the body. Depending on the health of the heart, doctors may recommend a heart transplant, but finding a suitable donor is often a long duration process and the patient might be at an advance condition or the patient is not adequate for a heart transplant. In such cases Ventricular assist devices (VAD) are implemented. The purpose of a VAD is to aid the heart to pump the correct amount...
Show moreHeart failure occurs when the heart is not capable to pump blood at a sufficient rate to meet the demands of the body. Depending on the health of the heart, doctors may recommend a heart transplant, but finding a suitable donor is often a long duration process and the patient might be at an advance condition or the patient is not adequate for a heart transplant. In such cases Ventricular assist devices (VAD) are implemented. The purpose of a VAD is to aid the heart to pump the correct amount of blood, by doing so it relives the load that is put on the heart while giving the patient a chance for recovery. This study focuses on observing the hemodynamic effects of implementing a left ventricular assist device (LVAD) along the aortic arch and main arteries. Thrombi creation and transportation is other subject included in the study, due to the fact that thrombi can obstruct blood flow to critical arteries, manly carotid and vertebral. Occlusion of these can lead to a stroke with devastating effects on the neurocognitive functions and even death.A multi-scale CFD analysis a patient specific geometry model is used as well as a lumped system which provides the correct conditions in order to simulate the whole cardiovascular system. The main goal of the study is to understand the difference in flow behavior created by the unsteady pulsatile boundary conditions. The model described in this work has a total cardiac output of 7.0 Liters/ minute, this for a healthy heart. Two cardiac output splits are used to simulate heart failure conditions. The first split consists of 5 Liters/minute flowing through the LVAD cannula and 2 Liters/minute via the aortic root. The second scenario is when heartivfailure is critical, meaning that zero flow is being output by the left ventricle, thus a split of 7 Liter/minute trough the LVAD cannula and 0 Liters/minute traveling through the aortic root. A statistical analysis for the thrombi motion throughout the patient aortic arch was performed in order to quantify the influence that pulsatile flow has on the particles being track. Spherical particles of 2mm, 4mm and 5mm were released and accounted in the statistical analysis for each of the two split configurations. The study focuses on particles that escaped on the outlet boundaries of the upper arteries (Right Carotid, Left Carotid, and Vertebral). Results exhibit the statistical comparison of means for each particle diameter as well as for the overall probability for the steady and unsteady flow condition.
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Date Issued: 2013
Identifier: CFE0004905, ucf:49633
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004905

Title: Visual Analysis of Extremely Dense Crowded Scenes.
Creator: Idrees, Haroon, Shah, Mubarak, Da Vitoria Lobo, Niels, Stanley, Kenneth, Atia, George, Saleh, Bahaa, University of Central Florida
Abstract / Description: Visual analysis of dense crowds is particularly challenging due to large number of individuals, occlusions, clutter, and fewer pixels per person which rarely occur in ordinary surveillance scenarios. This dissertation aims to address these challenges in images and videos of extremely dense crowds containing hundreds to thousands of humans. The goal is to tackle the fundamental problems of counting, detecting and tracking people in such images and videos using visual and contextual cues that...
Show moreVisual analysis of dense crowds is particularly challenging due to large number of individuals, occlusions, clutter, and fewer pixels per person which rarely occur in ordinary surveillance scenarios. This dissertation aims to address these challenges in images and videos of extremely dense crowds containing hundreds to thousands of humans. The goal is to tackle the fundamental problems of counting, detecting and tracking people in such images and videos using visual and contextual cues that are automatically derived from the crowded scenes.For counting in an image of extremely dense crowd, we propose to leverage multiple sources of information to compute an estimate of the number of individuals present in the image. Our approach relies on sources such as low confidence head detections, repetition of texture elements (using SIFT), and frequency-domain analysis to estimate counts, along with confidence associated with observing individuals, in an image region. Furthermore, we employ a global consistency constraint on counts using Markov Random Field which caters for disparity in counts in local neighborhoods and across scales. We tested this approach on crowd images with the head counts ranging from 94 to 4543 and obtained encouraging results. Through this approach, we are able to count people in images of high-density crowds unlike previous methods which are only applicable to videos of low to medium density crowded scenes. However, the counting procedure just outputs a single number for a large patch or an entire image. With just the counts, it becomes difficult to measure the counting error for a query image with unknown number of people. For this, we propose to localize humans by finding repetitive patterns in the crowd image. Starting with detections from an underlying head detector, we correlate them within the image after their selection through several criteria: in a pre-defined grid, locally, or at multiple scales by automatically finding the patches that are most representative of recurring patterns in the crowd image. Finally, the set of generated hypotheses is selected using binary integer quadratic programming with Special Ordered Set (SOS) Type 1 constraints.Human Detection is another important problem in the analysis of crowded scenes where the goal is to place a bounding box on visible parts of individuals. Primarily applicable to images depicting medium to high density crowds containing several hundred humans, it is a crucial pre-requisite for many other visual tasks, such as tracking, action recognition or detection of anomalous behaviors, exhibited by individuals in a dense crowd. For detecting humans, we explore context in dense crowds in the form of locally-consistent scale prior which captures the similarity in scale in local neighborhoods with smooth variation over the image. Using the scale and confidence of detections obtained from an underlying human detector, we infer scale and confidence priors using Markov Random Field. In an iterative mechanism, the confidences of detections are modified to reflect consistency with the inferred priors, and the priors are updated based on the new detections. The final set of detections obtained are then reasoned for occlusion using Binary Integer Programming where overlaps and relations between parts of individuals are encoded as linear constraints. Both human detection and occlusion reasoning in this approach are solved with local neighbor-dependent constraints, thereby respecting the inter-dependence between individuals characteristic to dense crowd analysis. In addition, we propose a mechanism to detect different combinations of body parts without requiring annotations for individual combinations.Once human detection and localization is performed, we then use it for tracking people in dense crowds. Similar to the use of context as scale prior for human detection, we exploit it in the form of motion concurrence for tracking individuals in dense crowds. The proposed method for tracking provides an alternative and complementary approach to methods that require modeling of crowd flow. Simultaneously, it is less likely to fail in the case of dynamic crowd flows and anomalies by minimally relying on previous frames. The approach begins with the automatic identification of prominent individuals from the crowd that are easy to track. Then, we use Neighborhood Motion Concurrence to model the behavior of individuals in a dense crowd, this predicts the position of an individual based on the motion of its neighbors. When the individual moves with the crowd flow, we use Neighborhood Motion Concurrence to predict motion while leveraging five-frame instantaneous flow in case of dynamically changing flow and anomalies. All these aspects are then embedded in a framework which imposes hierarchy on the order in which positions of individuals are updated. The results are reported on eight sequences of medium to high density crowds and our approach performs on par with existing approaches without learning or modeling patterns of crowd flow.We experimentally demonstrate the efficacy and reliability of our algorithms by quantifying the performance of counting, localization, as well as human detection and tracking on new and challenging datasets containing hundreds to thousands of humans in a given scene.
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Date Issued: 2014
Identifier: CFE0005508, ucf:50367
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005508

Title: MULTI-VIEW GEOMETRIC CONSTRAINTS FOR HUMAN ACTION RECOGNITION AND TRACKING.
Creator: GRITAI, ALEXEI, Shah, Mubarak, University of Central Florida
Abstract / Description: Human actions are the essence of a human life and a natural product of the human mind. Analysis of human activities by a machine has attracted the attention of many researchers. This analysis is very important in a variety of domains including surveillance, video retrieval, human-computer interaction, athlete performance investigation, etc. This dissertation makes three major contributions to automatic analysis of human actions. First, we conjecture that the relationship between body joints...
Show moreHuman actions are the essence of a human life and a natural product of the human mind. Analysis of human activities by a machine has attracted the attention of many researchers. This analysis is very important in a variety of domains including surveillance, video retrieval, human-computer interaction, athlete performance investigation, etc. This dissertation makes three major contributions to automatic analysis of human actions. First, we conjecture that the relationship between body joints of two actors in the same posture can be described by a 3D rigid transformation. This transformation simultaneously captures different poses and various sizes and proportions. As a consequence of this conjecture, we show that there exists a fundamental matrix between the imaged positions of the body joints of two actors, if they are in the same posture. Second, we propose a novel projection model for cameras moving at a constant velocity in 3D space, \emph cameras, and derive the Galilean fundamental matrix and apply it to human action recognition. Third, we propose a novel use for the invariant ratio of areas under an affine transformation and utilizing the epipolar geometry between two cameras for 2D model-based tracking of human body joints. In the first part of the thesis, we propose an approach to match human actions using semantic correspondences between human bodies. These correspondences are used to provide geometric constraints between multiple anatomical landmarks ( e.g. hands, shoulders, and feet) to match actions observed from different viewpoints and performed at different rates by actors of differing anthropometric proportions. The fact that the human body has approximate anthropometric proportion allows for innovative use of the machinery of epipolar geometry to provide constraints for analyzing actions performed by people of different anthropometric sizes, while ensuring that changes in viewpoint do not affect matching. A novel measure in terms of rank of matrix constructed only from image measurements of the locations of anatomical landmarks is proposed to ensure that similar actions are accurately recognized. Finally, we describe how dynamic time warping can be used in conjunction with the proposed measure to match actions in the presence of nonlinear time warps. We demonstrate the versatility of our algorithm in a number of challenging sequences and applications including action synchronization , odd one out, following the leader, analyzing periodicity etc. Next, we extend the conventional model of image projection to video captured by a camera moving at constant velocity. We term such moving camera Galilean camera. To that end, we derive the spacetime projection and develop the corresponding epipolar geometry between two Galilean cameras. Both perspective imaging and linear pushbroom imaging form specializations of the proposed model and we show how six different ``fundamental" matrices including the classic fundamental matrix, the Linear Pushbroom (LP) fundamental matrix, and a fundamental matrix relating Epipolar Plane Images (EPIs) are related and can be directly recovered from a Galilean fundamental matrix. We provide linear algorithms for estimating the parameters of the the mapping between videos in the case of planar scenes. For applying fundamental matrix between Galilean cameras to human action recognition, we propose a measure that has two important properties. First property makes it possible to recognize similar actions, if their execution rates are linearly related. Second property allows recognizing actions in video captured by Galilean cameras. Thus, the proposed algorithm guarantees that actions can be correctly matched despite changes in view, execution rate, anthropometric proportions of the actor, and even if the camera moves with constant velocity. Finally, we also propose a novel 2D model based approach for tracking human body parts during articulated motion. The human body is modeled as a 2D stick figure of thirteen body joints and an action is considered as a sequence of these stick figures. Given the locations of these joints in every frame of a model video and the first frame of a test video, the joint locations are automatically estimated throughout the test video using two geometric constraints. First, invariance of the ratio of areas under an affine transformation is used for initial estimation of the joint locations in the test video. Second, the epipolar geometry between the two cameras is used to refine these estimates. Using these estimated joint locations, the tracking algorithm determines the exact location of each landmark in the test video using the foreground silhouettes. The novelty of the proposed approach lies in the geometric formulation of human action models, the combination of the two geometric constraints for body joints prediction, and the handling of deviations in anthropometry of individuals, viewpoints, execution rate, and style of performing action. The proposed approach does not require extensive training and can easily adapt to a wide variety of articulated actions.
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Date Issued: 2007
Identifier: CFE0001692, ucf:47199
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001692

Title: TRACKING ERROR OF LEVERAGED AND INVERSE ETFS.
Creator: Romano, John, Gilkeson, Jim, University of Central Florida
Abstract / Description: Tracking ability of leveraged and inverse exchange traded funds can be very important to investors looking for a dependable return. If the investor wants to put their money on a certain index they feel strongly about, they expect their investment vehicle to track that return appropriately. Over the years, we have seen tremendous growth in the exchange traded fund industry. In 2006, leveraged and inverse funds were introduced to the market, allowing investors to take leveraged and directional...
Show moreTracking ability of leveraged and inverse exchange traded funds can be very important to investors looking for a dependable return. If the investor wants to put their money on a certain index they feel strongly about, they expect their investment vehicle to track that return appropriately. Over the years, we have seen tremendous growth in the exchange traded fund industry. In 2006, leveraged and inverse funds were introduced to the market, allowing investors to take leveraged and directional trades on indices. These investment vehicles can be traded as easily as any stock, and therefore need some attention. Since any novice investor can access and trade these funds, they need to be aware of the risks they are taking. In this study, I test whether the ProShares S&P tracking leveraged and inverse exchange traded funds track their appropriate index multiple as promised. I did this by running regressions on each fund against the appropriate multiple of their underlying indices. I did this for funds of different market capitalization, for different holding periods, and with different amounts of leverage, to compare how these funds track in different conditions. I found that the large cap funds tend to track the best, with the small cap funds tracking the worst. I also find that tracking error tends to increase with longer holding periods. I find that the distribution of excess returns becomes less normal over longer holding periods, and begins to flatten out and widen. There does not seem to be a concrete conclusion as to whether or not the amount of leverage affects the tracking ability of the funds. I end up with mixed results when comparing amounts of leverage by model fit and by tracking error. Direction also does not seem to play any role in the tracking ability of these funds.
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Date Issued: 2012
Identifier: CFH0004184, ucf:44893
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFH0004184

Title: Decision-making for Vehicle Path Planning.
Creator: Xu, Jun, Turgut, Damla, Zhang, Shaojie, Zhang, Wei, Hasan, Samiul, University of Central Florida
Abstract / Description: This dissertation presents novel algorithms for vehicle path planning in scenarios where the environment changes. In these dynamic scenarios the path of the vehicle needs to adapt to changes in the real world. In these scenarios, higher performance paths can be achieved if we are able to predict the future state of the world, by learning the way it evolves from historical data. We are relying on recent advances in the field of deep learning and reinforcement learning to learn appropriate...
Show moreThis dissertation presents novel algorithms for vehicle path planning in scenarios where the environment changes. In these dynamic scenarios the path of the vehicle needs to adapt to changes in the real world. In these scenarios, higher performance paths can be achieved if we are able to predict the future state of the world, by learning the way it evolves from historical data. We are relying on recent advances in the field of deep learning and reinforcement learning to learn appropriate world models and path planning behaviors.There are many different practical applications that map to this model. In this dissertation we propose algorithms for two applications that are very different in domain but share important formal similarities: the scheduling of taxi services in a large city and tracking wild animals with an unmanned aerial vehicle.The first application models a centralized taxi dispatch center in a big city. It is a multivariate optimization problem for taxi time scheduling and path planning. The first goal here is to balance the taxi service demand and supply ratio in the city. The second goal is to minimize passenger waiting time and taxi idle driving distance. We design different learning models that capture taxi demand and destination distribution patterns from historical taxi data. The predictions are evaluated with real-world taxi trip records. The predicted taxi demand and destination is used to build a taxi dispatch model. The taxi assignment and re-balance is optimized by solving a Mixed Integer Programming (MIP) problem.The second application concerns animal monitoring using an unmanned aerial vehicle (UAV) to search and track wild animals in a large geographic area. We propose two different path planing approaches for the UAV. The first one is based on the UAV controller solving Markov decision process (MDP). The second algorithms relies on the past recorded animal appearances. We designed a learning model that captures animal appearance patterns and predicts the distribution of future animal appearances. We compare the proposed path planning approaches with traditional methods and evaluated them in terms of collected value of information (VoI), message delay and percentage of events collected.
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Date Issued: 2019
Identifier: CFE0007557, ucf:52606
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007557

Title: Characterization of Turbulent Flame-Vortex Dynamics for Bluff Body Stabilized Flames.
Creator: Rising, Cal, Ahmed, Kareem, Ghosh, Ranajay, Bhattacharya, Samik, University of Central Florida
Abstract / Description: Modern propulsion systems primarily operate under highly turbulent conditions in order to promote greater efficiency through an increase in mixing. The focus of this thesis is to identify the turbulent flame-vortex interaction to provide insights into the turbulent combustion process. This work is accomplished through the use of turbulent ramjet-style combustor which is stabilized through use of a bluff-body. The facility is equipped with a custom turbulence generator to modulate the incoming...
Show moreModern propulsion systems primarily operate under highly turbulent conditions in order to promote greater efficiency through an increase in mixing. The focus of this thesis is to identify the turbulent flame-vortex interaction to provide insights into the turbulent combustion process. This work is accomplished through the use of turbulent ramjet-style combustor which is stabilized through use of a bluff-body. The facility is equipped with a custom turbulence generator to modulate the incoming turbulence levels to allow flames across various regimes to be analyzed. High-speed particle image velocimetry (PIV) and CH* chemiluminescence diagnostics are implemented to resolve the flow field and flame position. The flame-vortex interaction can be described by the vorticity transport which has four terms; vortex stretching, baroclinic torque, dilatation, and viscous diffusion. The vorticity mechanisms are calculated through the implementation of a Lagrangian tracking scheme, which allows for the individual mechanisms to be decomposed along the path of individual tracks. The mechanisms are compared across different turbulence levels to determine the effects of turbulence on the vorticity mechanisms. The mechanisms are calculated along the flame front as well to determine the individual effects of the vorticity mechanisms on the evolving structure of the turbulent premixed flame. The flame front curvature is also compared across the various turbulence conditions. The results confirm that as the flame-front experiences increased turbulence levels the combustion induced mechanisms of baroclinic torque and dilation decrease, while vortex stretching increases. This is a result of the turbulent energy exchange becoming the controlling factor within the flow-field. In addition, increased flame curvature is experience by the flame front due to increased local baroclinicity and turbulent energy exchange.
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Date Issued: 2019
Identifier: CFE0007714, ucf:52451
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007714

Title: A Narrative Research Study of Self-Selected Tracking on Motivation in 10th Grade English Language Arts Classes.
Creator: Greuel, Audra, Olan, Elsie, Puig, Enrique, Hewitt, Randall, University of Central Florida
Abstract / Description: The practice of tracking has had longstanding negative impacts on students, especially students in lower academic tracks. This research suggests that tracking develops the themes of a narrative of deficit through inequality and exclusion and impedes student motivation due to the negative implications. A common finding of outside research studies was that of disapproval for the current school organizational structure of tracking due to the negative consequences on students. Furthermore,...
Show moreThe practice of tracking has had longstanding negative impacts on students, especially students in lower academic tracks. This research suggests that tracking develops the themes of a narrative of deficit through inequality and exclusion and impedes student motivation due to the negative implications. A common finding of outside research studies was that of disapproval for the current school organizational structure of tracking due to the negative consequences on students. Furthermore, several research studies developed an outline of positive ways to advocate for a unifying system of educational change. Educational leaders should heed the suggestions of researchers to promote changes within the system to benefit marginalized students. Students' silenced narratives should be considered to promote voice within educational change. The purpose of this narrative research is to explore motivation through the overt and covert narratives of 10th grade English Language Arts students, who self-select higher and lower academic tracks at a large, southeastern United States, public high school through a qualitative unstructured questionnaire. This study also observes 10th grade English Language Arts students' ability to discuss these issues. Using information from a 10-question qualitative, unstructured questionnaire of twelve (12) research participants, this thesis explores the following questions: Research question one (RQ1): What are 10th grade English Language Arts students' attitudes towards higher and lower academic tracks?, Research question two (RQ2): What factors contribute to 10th grade English Language Arts students' motivation to self-select higher and lower academic tracks?
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Date Issued: 2019
Identifier: CFE0007640, ucf:52501
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007640

Title: OBJECT TRACKING AND ACTIVITY RECOGNITION IN VIDEO ACQUIRED USING MOBILE CAMERAS.
Creator: Yilmaz, Alper, Shah, Mubarak, University of Central Florida
Abstract / Description: Due to increasing demand on deployable surveillance systems in recent years, object tracking and activity recognition are receiving considerable attention in the research community. This thesis contributes to both the tracking and the activity recognition components of a surveillance system. In particular, for the tracking component, we propose two different approaches for tracking objects in video acquired by mobile cameras, each of which uses a different object shape representation. The...
Show moreDue to increasing demand on deployable surveillance systems in recent years, object tracking and activity recognition are receiving considerable attention in the research community. This thesis contributes to both the tracking and the activity recognition components of a surveillance system. In particular, for the tracking component, we propose two different approaches for tracking objects in video acquired by mobile cameras, each of which uses a different object shape representation. The first approach tracks the centroids of the objects in Forward Looking Infrared Imagery (FLIR) and is suitable for tracking objects that appear small in airborne video. The second approach tracks the complete contours of the objects, and is suitable for higher level vision problems, such as activity recognition, identification and classification. Using the contours tracked by the contour tracker, we propose a novel representation, called the action sketch, for recognizing human activities.Object Tracking in Airborne Imagery: Images obtained from an airborne vehicle generally appear small and can be represented by geometric shapes such as circle or rectangle. After detecting the object position in the first frame, the proposed object tracker models the intensity and the local standard deviation of the object region defined by the shape model. It then tracks the objects by computing the mean-shift vector that minimizes the distance between the kernel distribution for the hypothesized object and its prior. In cases when the ego-motion of the sensor causes the object to move more than the operational limits of the tracking module, a multi-resolution global motion compensation using the Gabor responses of consecutive frames is performed. The experiments performed on the AMCOM FLIR data set show the robustness of the proposed method, which combines automatic model update and global motion compensation into one framework.Contour Tracker: Contour tracking is performed by evolving an initial contour toward the correct object boundaries based on discriminant analysis, which is formulated as a variational calculus problem. Once the contour is initialized, the method generates an online shape model for the object along with the color and the texture priors for both the object and the background regions. A priori texture and color PDFs of the regions are then fused based on the discrimination properties of the features between the object and the background models. The models are then used to compute the posteriori contour likelihood and the evolution is obtained by the Maximum a Posteriori Estimation process, which updates the contour in the gradient ascent direction of the proposed energy functional. During occlusion, the online shape model is used to complete the missing object region. The proposed energy functional unifies commonly used boundary and region based contour approaches into a single framework through a support region defined around the hypothesized object contour. We tested the robustness of the proposed contour tracker using several real sequences and have verified qualitatively that the contours of the objects are perfectly tracked.Behavior Analysis: We propose a novel approach to represent human actions by modeling the dynamics (motion) and the structure (shape) of the objects in video. Both the motion and the shape are modeled using a compact representation, which is called the ``action sketch''. An action sketch is a view invariant representation obtained by analyzing important changes that occur during the motion of the objects. When an actor performs an action in 3D, the points on the actor generate space-time trajectories in four dimensions $(x,y,z,t)$. Projection of the world to the imaging coordinates converts the space-time trajectories into the spatio-temporal trajectories in three dimensions $(x,y,t)$. A set of spatio-temporal trajectories constitute a 3D volume, which we call an ``action volume''. This volum
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Date Issued: 2004
Identifier: CFE0000101, ucf:52858
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000101

Title: TOWARDS CALIBRATION OF OPTICAL FLOW OF CROWD VIDEOS USING OBSERVED TRAJECTORIES.
Creator: Elbadramany, Iman, Kaup, David, University of Central Florida
Abstract / Description: The need exists for finding a quantitative method for validating crowd simulations. One approach is to use optical flow of videos of real crowds to obtain velocities that can be used for comparison to simulations. Optical flow, in turn, needs to be calibrated to be useful. It is essential to show that optical flow velocities obtained from crowd videos can be mapped into the spatially averaged velocities of the observed trajectories of crowd members, and to quantify the extent of the...
Show moreThe need exists for finding a quantitative method for validating crowd simulations. One approach is to use optical flow of videos of real crowds to obtain velocities that can be used for comparison to simulations. Optical flow, in turn, needs to be calibrated to be useful. It is essential to show that optical flow velocities obtained from crowd videos can be mapped into the spatially averaged velocities of the observed trajectories of crowd members, and to quantify the extent of the correlation of the results. This research investigates methods to uncover the best conditions for a good correlation between optical flow and the average motion of individuals in crowd videos, with the aim that this will help in the quantitative validation of simulations. The first approach was to use a simple linear proportionality relation, with a single coefficient, alpha, between velocity vector of the optical flow and observed velocity of crowd members in a video or simulation. Since there are many variables that affect alpha, an attempt was made to find the best possible conditions for determining alpha, by varying experimental and optical flow settings. The measure of a good alpha was chosen to be that alpha does not vary excessively over a number of video frames. Best conditions of low coefficient of variation of alpha using the Lucas-Kanade optical flow algorithm were found to be when a larger aperture of 15x15 pixels was used, combined with a smaller threshold. Adequate results were found at cell size 40x40 pixels; the improvement in detecting details when smaller cells are used did not reduce the variability of alpha, and required much more computing power. Reduction in variability of alpha can be obtained by spreading the tracked location of a crowd member from a pixel into a rectangle. The Particle Image Velocimetry optical flow algorithm had better correspondence with the velocity vectors of manually tracked crowd members than results obtained using the Lukas-Kanade method. Here, also, it was found that 40x40 pixel cells were better than 15x15. A second attempt at quantifying the correlation between optical flow and actual crowd member velocities was studied using simulations. Two processes were researched, which utilized geometrical correction of the perspective distortion of the crowd videos. One process geometrically corrects the video, and then obtains optical flow data. The other obtains optical flow data from video, and then geometrically corrects the data. The results indicate that the first process worked better. Correlation was calculated between sets of data obtained from the average of twenty frames. This was found to be higher than calculating correlations between the velocities of cells in each pair of frames. An experiment was carried out to predict crowd tracks using optical flow and a calculated parameter, beta, seems to give promising results.
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Date Issued: 2011
Identifier: CFE0004024, ucf:49175
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004024

Title: An investigation of the relationship between visual effects and object identification using eye-tracking.
Creator: Rosch, Jonathan, Schoenfeld, Winston, Likamwa, Patrick, Wu, Shintson, Vogel-Walcutt, Jennifer, University of Central Florida
Abstract / Description: The visual content represented on information displays used in training environments prescribe display attributes as brightness, color, contrast, and motion blur, but considerations regarding cognitive processes corresponding to these visual features require further attention in order to optimize the display for training applications. This dissertation describes an empirical study with which information display features, specifically color and motion blur reduction, were investigated to...
Show moreThe visual content represented on information displays used in training environments prescribe display attributes as brightness, color, contrast, and motion blur, but considerations regarding cognitive processes corresponding to these visual features require further attention in order to optimize the display for training applications. This dissertation describes an empirical study with which information display features, specifically color and motion blur reduction, were investigated to assess their impact in a training scenario involving visual search and threat detection. Presented in this document is a review of the theory and literature describing display technology, its applications to training, and how eye-tracking systems can be used to objectively measure cognitive activity. The experiment required participants to complete a threat identification task, while altering the displays settings beforehand, to assess the utility of the display capabilities. The data obtained led to the conclusion that motion blur had a stronger impact on perceptual load than the addition of color. The increased perceptual load resulted in approximately 8-10% longer fixation durations for all display conditions and a similar decrease in the number of saccades, but only when motion blur reduction was used. No differences were found in terms of threat location or threat identification accuracy, so it was concluded that the effects of perceptual load were independent of germane cognitive load.
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Date Issued: 2012
Identifier: CFE0004591, ucf:49219
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004591

Title: MARKERLESS TRACKING USING POLAR CORRELATION OF CAMERA OPTICAL FLOW.
Creator: Gupta, Prince, da Vitoria Lobo, Niels, University of Central Florida
Abstract / Description: We present a novel, real-time, markerless vision-based tracking system, employing a rigid orthogonal configuration of two pairs of opposing cameras. Our system uses optical flow over sparse features to overcome the limitation of vision-based systems that require markers or a pre-loaded model of the physical environment. We show how opposing cameras enable cancellation of common components of optical flow leading to an efficient tracking algorithm that captures five degrees of freedom...
Show moreWe present a novel, real-time, markerless vision-based tracking system, employing a rigid orthogonal configuration of two pairs of opposing cameras. Our system uses optical flow over sparse features to overcome the limitation of vision-based systems that require markers or a pre-loaded model of the physical environment. We show how opposing cameras enable cancellation of common components of optical flow leading to an efficient tracking algorithm that captures five degrees of freedom including direction of translation and angular velocity. Experiments comparing our device with an electromagnetic tracker show that its average tracking accuracy is 80% over 185 frames, and it is able to track large range motions even in outdoor settings. We also present how opposing cameras in vision-based inside-looking-out systems can be used for gesture recognition. To demonstrate our approach, we discuss three different algorithms for recovering motion parameters at different levels of complete recovery. We show how optical flow in opposing cameras can be used to recover motion parameters of the multi-camera rig. Experimental results show gesture recognition accuracy of 88.0%, 90.7% and 86.7% for our three techniques, respectively, across a set of 15 gestures.
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Date Issued: 2010
Identifier: CFE0003163, ucf:48611
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003163

Title: EMPIRICAL EVALUATION OF THE EFFECTIVENESS OF EYE TRACKING-BASED SEARCH PERFORMANCE DIAGNOSIS AND FEEDBACK METHODS.
Creator: Carroll, Meredith, Mouloua, Mustapha, University of Central Florida
Abstract / Description: In todayÃ‚Â's complex combat environments, troops are often faced with increasingly challenging tasks different from those experienced in the past. Warfighters must be trained in adaptive perceptual skill sets, such as search strategies that enable them to detect threats across any number of environmental, cultural, and situational conditions. The goal of the present study was to explore how advanced technology, specifically eye tracking, can be used to increase...
Show moreIn todayÃ‚Â's complex combat environments, troops are often faced with increasingly challenging tasks different from those experienced in the past. Warfighters must be trained in adaptive perceptual skill sets, such as search strategies that enable them to detect threats across any number of environmental, cultural, and situational conditions. The goal of the present study was to explore how advanced technology, specifically eye tracking, can be used to increase understanding of perceptual processes such as search and detection and provide tools that can be used to train search skills. Experiment 1 examined a method of diagnosing perceptual performance in order to be able to identify the perceptual root cause of target detection deficiencies and how these impact overall target detection performance. Findings indicate the method can be used to pinpoint where in the perceptual process a target miss originated, whether due to ineffective search strategy, inability to detect the subtle cues of the threat or inability to recognize these cues as indicative of a threat. Experiment 2 examined the training effectiveness of providing trainees with process level tailored feedback which incorporates elements of expert and trainee scan patterns. Findings indicate that providing trainees with elements of either expert or trainee scan patterns has the ability to significantly improve the search strategy being employed by the trainee. This work provides strong support for the use of eye tracking based perceptual performance diagnosis methods and training strategies in improving trainee search performance for complex target detection tasks.
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Date Issued: 2010
Identifier: CFE0003100, ucf:48302
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003100

Title: Geolocation of Diseased Leaves in Strawberry Orchards for a Custom-Designed Octorotor.
Creator: Garcia, Christian, Xu, Yunjun, Lin, Kuo-Chi, Kauffman, Jeffrey, University of Central Florida
Abstract / Description: In recent years, technological advances have shown a strive for more automated processes in agriculture, as seem with the use of unmanned aerial vehicles (UAVs) with onboard sensors in many applications, including disease detection and yield prediction. In this thesis, an octorotor UAV is presented that was designed, built, and flight tested, with features that are custom-designed for strawberry orchard disease detection. To further automate the disease scouting operation, geolocation, or the...
Show moreIn recent years, technological advances have shown a strive for more automated processes in agriculture, as seem with the use of unmanned aerial vehicles (UAVs) with onboard sensors in many applications, including disease detection and yield prediction. In this thesis, an octorotor UAV is presented that was designed, built, and flight tested, with features that are custom-designed for strawberry orchard disease detection. To further automate the disease scouting operation, geolocation, or the process of determining global position coordinates of identified diseased regions based on images taken, is investigated. A Kalman filter is designed, based on a linear measurement model derived from an orthographic projection method, to estimate the target position. Simulation, as well as an ad-hoc experiment using flight data, is performed to compare this filter to the extended Kalman filter (EKF), which is based on the commonly used perspective projection method. The filter is embedded onto a CPU board for real-time use aboard the octorotor UAV, and the algorithm structure for this process is presented. In the later part of the thesis, a probabilistic data association method is used, jointly with a proposed logic-based measurement-to-target correlation method, to analyze measurements of different target sources and is incorporated into the Kalman filter. A simulation and an ad-hoc experiment, using video and flight data acquired aboard the octorotor UAV with a gimballed camera in hover flight, are performed to demonstrate the effectiveness of the algorithm and UAV platform.
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Date Issued: 2016
Identifier: CFE0006305, ucf:51597
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006305

Title: Global Data Association for Multiple Pedestrian Tracking.
Creator: Dehghan, Afshin, Shah, Mubarak, Qi, GuoJun, Bagci, Ulas, Zhang, Shaojie, Zheng, Qipeng, University of Central Florida
Abstract / Description: Multi-object tracking is one of the fundamental problems in computer vision. Almost all multi-object tracking systems consist of two main components; detection and data association. In the detection step, object hypotheses are generated in each frame of a sequence. Later, detections that belong to the same target are linked together to form final trajectories. The latter step is called data association. There are several challenges that render this problem difficult, such as occlusion,...
Show moreMulti-object tracking is one of the fundamental problems in computer vision. Almost all multi-object tracking systems consist of two main components; detection and data association. In the detection step, object hypotheses are generated in each frame of a sequence. Later, detections that belong to the same target are linked together to form final trajectories. The latter step is called data association. There are several challenges that render this problem difficult, such as occlusion, background clutter and pose changes. This dissertation aims to address these challenges by tackling the data association component of tracking and contributes three novel methods for solving data association. Firstly, this dissertation will present a new framework for multi-target tracking that uses a novel data association technique using the Generalized Maximum Clique Problem (GMCP) formulation. The majority of current methods, such as bipartite matching, incorporate a limited temporal locality of the sequence into the data association problem. This makes these methods inherently prone to ID-switches and difficulties caused by long-term occlusions, a cluttered background and crowded scenes. On the other hand, our approach incorporates both motion and appearance in a global manner. Unlike limited temporal locality methods which incorporate a few frames into the data association problem, this method incorporates the whole temporal span and solves the data association problem for one object at a time. Generalized Minimum Clique Graph (GMCP) is used to solve the optimization problem of our data association method. The proposed method is supported by superior results on several benchmark sequences. GMCP leads us to a more accurate approach to multi-object tracking by considering all the pairwise relationships in a batch of frames; however, it has some limitations. Firstly, it finds target trajectories one-by-one, missing joint optimization. Secondly, for optimization we use a greedy solver, based on local neighborhood search, making our optimization prone to local minimas. Finally GMCP tracker is slow, which is a burden when dealing with time-sensitive applications. In order to address these problems, we propose a new graph theoretic problem, called Generalized Maximum Multi Clique Problem (GMMCP). GMMCP tracker has all the advantages of the GMCP tracker while addressing its limitations. A solution is presented to GMMCP where no simplification is assumed in problem formulation or problem optimization. GMMCP is NP hard but it can be formulated through a Binary-Integer Program where the solution to small- and medium-sized tracking problems can be found efficiently. To improve speed, Aggregated Dummy Nodes are used for modeling occlusions and miss detections. This also reduces the size of the input graph without using any heuristics. We show that using the speed-up method, our tracker lends itself to a real-time implementation, increasing its potential usefulness in many applications. In test against several tracking datasets, we show that the proposed method outperforms competitive methods. Thus far we have assumed that the number of people do not exceed a few dozens. However, this is not always the case. In many scenarios such as, marathon, political rallies or religious rites, the number of people in a frame may reach few hundreds or even few thousands. Tracking in high-density crowd sequences is a challenging problem due to several reasons. Human detection methods often fail to localize objects correctly in extremely crowded scenes. This limits the use of data association based tracking methods. Additionally, it is hard to extend existing multi-target tracking to track targets in highly-crowded scenes, because the large number of targets increases the computational complexity. Furthermore, the small apparent target size makes it challenging to extract features to discriminate targets from their surroundings. Finally, we present a tracker that addresses the above-mentioned problems. We formulate online crowd tracking as a Binary Quadratic Programing, where both detection and data association problems are solved together. Our formulation employs target's individual information in the form of appearance and motion as well as contextual cues in the form of neighborhood motion, spatial proximity and grouping constraints. Due to large number of targets, state-of-the-art commercial quadratic programing solvers fail to efficiently find the solution to the proposed optimization. In order to overcome the computational complexity of available solvers, we propose to use the most recent version of Modified Frank-Wolfe algorithms with SWAP steps. The proposed tracker can track hundreds of targets efficiently and improves state-of-the-art results by significant margin on high density crowd sequences.
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Date Issued: 2016
Identifier: CFE0006095, ucf:51201
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006095

Title: Human Detection, Tracking and Segmentation in Surveillance Video.
Creator: Shu, Guang, Shah, Mubarak, Boloni, Ladislau, Wang, Jun, Lin, Mingjie, Sugaya, Kiminobu, University of Central Florida
Abstract / Description: This dissertation addresses the problem of human detection and tracking in surveillance videos. Even though this is a well-explored topic, many challenges remain when confronted with data from real world situations. These challenges include appearance variation, illumination changes, camera motion, cluttered scenes and occlusion. In this dissertation several novel methods for improving on the current state of human detection and tracking based on learning scene-specific information in video...
Show moreThis dissertation addresses the problem of human detection and tracking in surveillance videos. Even though this is a well-explored topic, many challenges remain when confronted with data from real world situations. These challenges include appearance variation, illumination changes, camera motion, cluttered scenes and occlusion. In this dissertation several novel methods for improving on the current state of human detection and tracking based on learning scene-specific information in video feeds are proposed.Firstly, we propose a novel method for human detection which employs unsupervised learning and superpixel segmentation. The performance of generic human detectors is usually degraded in unconstrained video environments due to varying lighting conditions, backgrounds and camera viewpoints. To handle this problem, we employ an unsupervised learning framework that improves the detection performance of a generic detector when it is applied to a particular video. In our approach, a generic DPM human detector is employed to collect initial detection examples. These examples are segmented into superpixels and then represented using Bag-of-Words (BoW) framework. The superpixel-based BoW feature encodes useful color features of the scene, which provides additional information. Finally a new scene-specific classifier is trained using the BoW features extracted from the new examples. Compared to previous work, our method learns scene-specific information through superpixel-based features, hence it can avoid many false detections typically obtained by a generic detector. We are able to demonstrate a significant improvement in the performance of the state-of-the-art detector.Given robust human detection, we propose a robust multiple-human tracking framework using a part-based model. Human detection using part models has become quite popular, yet its extension in tracking has not been fully explored. Single camera-based multiple-person tracking is often hindered by difficulties such as occlusion and changes in appearance. We address such problems by developing an online-learning tracking-by-detection method. Our approach learns part-based person-specific Support Vector Machine (SVM) classifiers which capture articulations of moving human bodies with dynamically changing backgrounds. With the part-based model, our approach is able to handle partial occlusions in both the detection and the tracking stages. In the detection stage, we select the subset of parts which maximizes the probability of detection. This leads to a significant improvement in detection performance in cluttered scenes. In the tracking stage, we dynamically handle occlusions by distributing the score of the learned person classifier among its corresponding parts, which allows us to detect and predict partial occlusions and prevent the performance of the classifiers from being degraded. Extensive experiments using the proposed method on several challenging sequences demonstrate state-of-the-art performance in multiple-people tracking.Next, in order to obtain precise boundaries of humans, we propose a novel method for multiple human segmentation in videos by incorporating human detection and part-based detection potential into a multi-frame optimization framework. In the first stage, after obtaining the superpixel segmentation for each detection window, we separate superpixels corresponding to a human and background by minimizing an energy function using Conditional Random Field (CRF). We use the part detection potentials from the DPM detector, which provides useful information for human shape. In the second stage, the spatio-temporal constraints of the video is leveraged to build a tracklet-based Gaussian Mixture Model for each person, and the boundaries are smoothed by multi-frame graph optimization. Compared to previous work, our method could automatically segment multiple people in videos with accurate boundaries, and it is robust to camera motion. Experimental results show that our method achieves better segmentation performance than previous methods in terms of segmentation accuracy on several challenging video sequences.Most of the work in Computer Vision deals with point solution; a specific algorithm for a specific problem. However, putting different algorithms into one real world integrated system is a big challenge. Finally, we introduce an efficient tracking system, NONA, for high-definition surveillance video. We implement the system using a multi-threaded architecture (Intel Threading Building Blocks (TBB)), which executes video ingestion, tracking, and video output in parallel. To improve tracking accuracy without sacrificing efficiency, we employ several useful techniques. Adaptive Template Scaling is used to handle the scale change due to objects moving towards a camera. Incremental Searching and Local Frame Differencing are used to resolve challenging issues such as scale change, occlusion and cluttered backgrounds. We tested our tracking system on a high-definition video dataset and achieved acceptable tracking accuracy while maintaining real-time performance.
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Date Issued: 2014
Identifier: CFE0005551, ucf:50278
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005551

Title: Scene Understanding for Real Time Processing of Queries over Big Data Streaming Video.
Creator: Aved, Alexander, Hua, Kien, Foroosh, Hassan, Zou, Changchun, Ni, Liqiang, University of Central Florida
Abstract / Description: With heightened security concerns across the globe and the increasing need to monitor, preserve and protect infrastructure and public spaces to ensure proper operation, quality assurance and safety, numerous video cameras have been deployed. Accordingly, they also need to be monitored effectively and efficiently. However, relying on human operators to constantly monitor all the video streams is not scalable or cost effective. Humans can become subjective, fatigued, even exhibit bias and it is...
Show moreWith heightened security concerns across the globe and the increasing need to monitor, preserve and protect infrastructure and public spaces to ensure proper operation, quality assurance and safety, numerous video cameras have been deployed. Accordingly, they also need to be monitored effectively and efficiently. However, relying on human operators to constantly monitor all the video streams is not scalable or cost effective. Humans can become subjective, fatigued, even exhibit bias and it is difficult to maintain high levels of vigilance when capturing, searching and recognizing events that occur infrequently or in isolation.These limitations are addressed in the Live Video Database Management System (LVDBMS), a framework for managing and processing live motion imagery data. It enables rapid development of video surveillance software much like traditional database applications are developed today. Such developed video stream processing applications and ad hoc queries are able to "reuse" advanced image processing techniques that have been developed. This results in lower software development and maintenance costs. Furthermore, the LVDBMS can be intensively tested to ensure consistent quality across all associated video database applications. Its intrinsic privacy framework facilitates a formalized approach to the specification and enforcement of verifiable privacy policies. This is an important step towards enabling a general privacy certification for video surveillance systems by leveraging a standardized privacy specification language.With the potential to impact many important fields ranging from security and assembly line monitoring to wildlife studies and the environment, the broader impact of this work is clear. The privacy framework protects the general public from abusive use of surveillance technology; success in addressing the (")trust(") issue will enable many new surveillance-related applications. Although this research focuses on video surveillance, the proposed framework has the potential to support many video-based analytical applications.
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Date Issued: 2013
Identifier: CFE0004648, ucf:49900
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004648

Tracking (7)	+ -
tracking (7)	+ -
MPPT (3)	+ -
Maximum Power Point Tracking (3)	+ -
Object Tracking (3)	+ -

optical flow (3)	+ -
surveillance (3)	+ -
Digital Control (2)	+ -
ECG (2)	+ -
EEG (2)	+ -
Eye-tracking (2)	+ -
Lagrangian Tracking (2)	+ -
Object Detection (2)	+ -
TCD (2)	+ -
UAV (2)	+ -
action recognition (2)	+ -
anomaly detection (2)	+ -
computer vision (2)	+ -
eye tracking (2)	+ -
fNIR (2)	+ -
2D Human Model Based Tracking (1)	+ -
3D User Interface (1)	+ -
3D reconstruction (1)	+ -
ADHD (1)	+ -
Adaptive (1)	+ -
Air-combat game (1)	+ -
Animal tracking (1)	+ -
Assessment (1)	+ -
Assit Device (1)	+ -
Attitude Determination (1)	+ -

ETD Collection (54)	+ -
Honors Collection (2)	+ -

University of Central Florida (56)	+ -
Shah, Mubarak (12)	+ -
Batarseh, Issa (4)	+ -
Qu, Zhihua (3)	+ -
Reinerman, Lauren (3)	+ -

Xu, Yunjun (3)	+ -
Atia, George (2)	+ -
Bagci, Ulas (2)	+ -
Da Vitoria Lobo, Niels (2)	+ -
Haralambous, Michael (2)	+ -
Kasparis, Takis (2)	+ -
Kassab, Alain (2)	+ -
Lin, Kuo-Chi (2)	+ -
Stanley, Kenneth (2)	+ -
Szalma, James (2)	+ -
Turgut, Damla (2)	+ -
Zhang, Shaojie (2)	+ -
Abich, Julian (1)	+ -
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