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CONFORMAL TRACKING FOR VIRTUAL ENVIRONMENTS
Title
CONFORMAL TRACKING FOR VIRTUAL ENVIRONMENTS.
Creator
Davis, Jr., Larry Dennis, Rolland, Jannick P., University of Central Florida
Abstract / Description
A virtual environment is a set of surroundings that appears to exist to a user through sensory stimuli provided by a computer. By virtual environment, we mean to include environments supporting the full range from VR to pure reality. A necessity for virtual environments is knowledge of the location of objects in the environment. This is referred to as the tracking problem, which points to the need for accurate and precise tracking in virtual environments.Marker-based tracking is a technique...
Show moreA virtual environment is a set of surroundings that appears to exist to a user through sensory stimuli provided by a computer. By virtual environment, we mean to include environments supporting the full range from VR to pure reality. A necessity for virtual environments is knowledge of the location of objects in the environment. This is referred to as the tracking problem, which points to the need for accurate and precise tracking in virtual environments.Marker-based tracking is a technique which employs fiduciary marks to determine the pose of a tracked object. A collection of markers arranged in a rigid configuration is called a tracking probe. The performance of marker-based tracking systems depends upon the fidelity of the pose estimates provided by tracking probes.The realization that tracking performance is linked to probe performance necessitates investigation into the design of tracking probes for proponents of marker-based tracking. The challenges involved with probe design include prediction of the accuracy and precision of a tracking probe, the creation of arbitrarily-shaped tracking probes, and the assessment of the newly created probes.To address these issues, we present a pioneer framework for designing conformal tracking probes. Conformal in this work means to adapt to the shape of the tracked objects and to the environmental constraints. As part of the framework, the accuracy in position and orientation of a given probe may be predicted given the system noise. The framework is a methodology for designing tracking probes based upon performance goals and environmental constraints. After presenting the conformal tracking framework, the elements used for completing the steps of the framework are discussed. We start with the application of optimization methods for determining the probe geometry. Two overall methods for mapping markers on tracking probes are presented, the Intermediary Algorithm and the Viewpoints Algorithm.Next, we examine the method used for pose estimation and present a mathematical model of error propagation used for predicting probe performance in pose estimation. The model uses a first-order error propagation, perturbing the simulated marker locations with Gaussian noise. The marker locations with error are then traced through the pose estimation process and the effects of the noise are analyzed. Moreover, the effects of changing the probe size or the number of markers are discussed.Finally, the conformal tracking framework is validated experimentally. The assessment methods are divided into simulation and post-fabrication methods. Under simulation, we discuss testing of the performance of each probe design. Then, post-fabrication assessment is performed, including accuracy measurements in orientation and position. The framework is validated with four tracking probes. The first probe is a six-marker planar probe. The predicted accuracy of the probe was 0.06 deg and the measured accuracy was 0.083 plus/minus 0.015 deg. The second probe was a pair of concentric, planar tracking probes mounted together. The smaller probe had a predicted accuracy of 0.206 deg and a measured accuracy of 0.282 plus/minus 0.03 deg. The larger probe had a predicted accuracy of 0.039 deg and a measured accuracy of 0.017 plus/minus 0.02 deg. The third tracking probe was a semi-spherical head tracking probe. The predicted accuracy in orientation and position was 0.54 plus/minus 0.24 deg and 0.24 plus/minus 0.1 mm, respectively. The experimental accuracy in orientation and position was 0.60 plus/minus 0.03 deg and 0.225 plus/minus 0.05 mm, respectively. The last probe was an integrated, head-mounted display probe, created using the conformal design process. The predicted accuracy of this probe was 0.032 plus/minus 0.02 degrees in orientation and 0.14 plus/minus 0.08 mm in position. The measured accuracy of the probe was 0.028 plus/minus 0.01 degrees in orientation and 0.11 plus/minus 0.01 mm in position
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Date Issued
2004
Identifier
CFE0000058, ucf:52856
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000058
Spatiotemporal Graphs for Object Segmentation and Human Pose Estimation in Videos
Title
Spatiotemporal Graphs for Object Segmentation and Human Pose Estimation in Videos.
Creator
Zhang, Dong, Shah, Mubarak, Qi, GuoJun, Bagci, Ulas, Yun, Hae-Bum, University of Central Florida
Abstract / Description
Images and videos can be naturally represented by graphs, with spatial graphs for images and spatiotemporal graphs for videos. However, for different applications, there are usually different formulations of the graphs, and algorithms for each formulation have different complexities. Therefore, wisely formulating the problem to ensure an accurate and efficient solution is one of the core issues in Computer Vision research. We explore three problems in this domain to demonstrate how to...
Show moreImages and videos can be naturally represented by graphs, with spatial graphs for images and spatiotemporal graphs for videos. However, for different applications, there are usually different formulations of the graphs, and algorithms for each formulation have different complexities. Therefore, wisely formulating the problem to ensure an accurate and efficient solution is one of the core issues in Computer Vision research. We explore three problems in this domain to demonstrate how to formulate all of these problems in terms of spatiotemporal graphs and obtain good and efficient solutions.The first problem we explore is video object segmentation. The goal is to segment the primary moving objects in the videos. This problem is important for many applications, such as content based video retrieval, video summarization, activity understanding and targeted content replacement. In our framework, we use object proposals, which are object-like regions obtained by low-level visual cues. Each object proposal has an object-ness score associated with it, which indicates how likely this object proposal corresponds to an object. The problem is formulated as a directed acyclic graph, for which nodes represent the object proposals and edges represent the spatiotemporal relationship between nodes. A dynamic programming solution is employed to select one object proposal from each video frame, while ensuring their consistency throughout the video frames. Gaussian mixture models (GMMs) are used for modeling the background and foreground, and Markov Random Fields (MRFs) are employed to smooth the pixel-level segmentation.In the above spatiotemporal graph formulation, we consider the object segmentation in only single video. Next, we consider multiple videos and model the video co-segmentation problem as a spatiotemporal graph. The goal here is to simultaneously segment the moving objects from multiple videos and assign common objects the same labels. The problem is formulated as a regulated maximum clique problem using object proposals. The object proposals are tracked in adjacent frames to generate a pool of candidate tracklets. Then an undirected graph is built with the nodes corresponding to the tracklets from all the videos and edges representing the similarities between the tracklets. A modified Bron-Kerbosch Algorithm is applied to the graph in order to select the prominent objects contained in these videos, hence relate the segmentation of each object in different videos.In online and surveillance videos, the most important object class is the human. In contrast to generic video object segmentation and co-segmentation, specific knowledge about humans, which is defined by a pose (i.e. human skeleton), can be employed to help the segmentation and tracking of people in the videos. We formulate the problem of human pose estimation in videos using the spatiotemporal graph. In this formulation, the nodes represent different body parts in the video frames and edges represent the spatiotemporal relationship between body parts in adjacent frames. The graph is carefully designed to ensure an exact and efficient solution. The overall objective for the new formulation is to remove the simple cycles from the traditional graph-based formulations. Dynamic programming is employed in different stages in the method to select the best tracklets and human pose configurations
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Date Issued
2016
Identifier
CFE0006429, ucf:51488
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006429