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Applications of Computer Vision, IEEE Workshop on (2008)
Copper Mountain, CO, USA
Jan. 7, 2008 to Jan. 9, 2008
ISBN: 978-1-4244-1913-5
pp: 1-7
Aniruddha Kembhavi , Department of Electrical Engineering, Department of Computer Science, University of Maryland, College Park, MD 20742, anikem@umd.edu
Ryan Farrell , Department of Electrical Engineering, Department of Computer Science, University of Maryland, College Park, MD 20742, farrell@cs.umd.edu
Yuancheng Luo , Department of Electrical Engineering, Department of Computer Science, University of Maryland, College Park, MD 20742, yluo1@umd.edu
David Jacobs , Department of Electrical Engineering, Department of Computer Science, University of Maryland, College Park, MD 20742,djacobs@umiacs.umd.edu
Ramani Duraiswami , Department of Electrical Engineering, Department of Computer Science, University of Maryland, College Park, MD 20742, ramani@umiacs.umd.edu
Larry S. Davis , Department of Electrical Engineering, Department of Computer Science, University of Maryland, College Park, MD 20742, lsd@cs.umd.edu
ABSTRACT
Sociobiologists collect huge volumes of video to study animal behavior (our collaborators work with 30,000 hours of video). The scale of these datasets demands the development of automated video analysis tools. Detecting and tracking animals is a critical first step in this process. However, off-the-shelf methods prove incapable of handling videos characterized by poor quality, drastic illumination changes, non-stationary scenery and foreground objects that become motionless for long stretches of time. We improve on existing approaches by taking advantage of specific aspects of this problem: by using information from the entire video we are able to find animals that become motionless for long intervals of time; we make robust decisions based on regional features; for different parts of the image, we tailor the selection of model features, choosing the features most helpful in differentiating the target animal from the background in that part of the image. We evaluate our method, achieving almost 83% tracking accuracy on a more than 200,000 frame dataset of Satin Bowerbird courtship videos.
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CITATION

R. Farrell, D. Jacobs, Y. Luo, R. Duraiswami, L. S. Davis and A. Kembhavi, "Tracking Down Under: Following the Satin Bowerbird," Applications of Computer Vision, IEEE Workshop on(WACV), Copper Mountain, CO, USA, 2008, pp. 1-7.
doi:10.1109/WACV.2008.4544004
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