Current Search: UAV -- aerial video -- metadata -- shadow -- detection -- human -- vehicle -- pedestrian -- tracking -- surveillance -- WAAS -- action -- recognition -- one-shot -- SVM -- fusion (x)
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Title
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Detecting, Tracking, and Recognizing Activities in Aerial Video.
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Creator
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Reilly, Vladimir, Shah, Mubarak, Georgiopoulos, Michael, Stanley, Kenneth, Dogariu, Aristide, University of Central Florida
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Abstract / Description
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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...
Show moreIn 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.
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Date Issued
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2012
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Identifier
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CFE0004627, ucf:49935
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0004627
