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Detecting, Tracking, and Recognizing Activities in Aerial Video

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Date Issued:
2012
Abstract/Description:
In this dissertation we address the problem of detecting humans and vehicles, tracking their identities in crowded scenes, and finally determining human activities. First, we tackle the problem of detecting moving as well as stationary objects in scenes that contain parallax and shadows. We constrain the search of pedestrians and vehicles by representing them as shadow casting out of plane or (SCOOP) objects.Next, we propose a novel method for tracking a large number of densely moving objects in aerial video. We divide the scene into grid cells to define a set of local scene constraints which we use as part of the matching cost function to solve the tracking problem which allows us to track fast-moving objects in low frame rate videos.Finally, we propose a method for recognizing human actions from few examples. We use the bag of words action representation, assume that most of the classes have many examples, and construct Support Vector Machine models for each class. We then use Support Vector Machines for classes with many examples to improve the decision function of the Support Vector Machine that was trained using few examples via late fusion of weighted decision values.
Title: Detecting, Tracking, and Recognizing Activities in Aerial Video.
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Name(s): Reilly, Vladimir, Author
Shah, Mubarak, Committee Chair
Georgiopoulos, Michael, Committee Member
Stanley, Kenneth, Committee Member
Dogariu, Aristide, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2012
Publisher: University of Central Florida
Language(s): English
Abstract/Description: In this dissertation we address the problem of detecting humans and vehicles, tracking their identities in crowded scenes, and finally determining human activities. First, we tackle the problem of detecting moving as well as stationary objects in scenes that contain parallax and shadows. We constrain the search of pedestrians and vehicles by representing them as shadow casting out of plane or (SCOOP) objects.Next, we propose a novel method for tracking a large number of densely moving objects in aerial video. We divide the scene into grid cells to define a set of local scene constraints which we use as part of the matching cost function to solve the tracking problem which allows us to track fast-moving objects in low frame rate videos.Finally, we propose a method for recognizing human actions from few examples. We use the bag of words action representation, assume that most of the classes have many examples, and construct Support Vector Machine models for each class. We then use Support Vector Machines for classes with many examples to improve the decision function of the Support Vector Machine that was trained using few examples via late fusion of weighted decision values.
Identifier: CFE0004627 (IID), ucf:49935 (fedora)
Note(s): 2012-08-01
Ph.D.
Engineering and Computer Science, Computer Science
Doctoral
This record was generated from author submitted information.
Subject(s): UAV -- aerial video -- metadata -- shadow -- detection -- human -- vehicle -- pedestrian -- tracking -- surveillance -- WAAS -- action -- recognition -- one-shot -- SVM -- fusion
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0004627
Restrictions on Access: public 2013-02-15
Host Institution: UCF

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