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Green Image
Issue No. 05 - May (2013 vol. 35)
ISSN: 0162-8828
pp: 1248-1262
V. I. Morariu , Dept. of Comput. Sci., Univ. of Maryland, College Park, MD, USA
D. Harwood , Dept. of Comput. Sci., Univ. of Maryland, College Park, MD, USA
L. S. Davis , Dept. of Comput. Sci., Univ. of Maryland, College Park, MD, USA
We describe a framework that leverages mixed probabilistic and deterministic networks and their AND/OR search space to efficiently find and track the hands and feet of multiple interacting humans in 2D from a single camera view. Our framework detects and tracks multiple people's heads, hands, and feet through partial or full occlusion; requires few constraints (does not require multiple views, high image resolution, knowledge of performed activities, or large training sets); and makes use of constraints and AND/OR Branch-and-Bound with lazy evaluation and carefully computed bounds to efficiently solve the complex network that results from the consideration of interperson occlusion. Our main contributions are: 1) a multiperson part-based formulation that emphasizes extremities and allows for the globally optimal solution to be obtained in each frame, and 2) an efficient and exact optimization scheme that relies on AND/OR Branch-and-Bound, lazy factor evaluation, and factor cost sensitive bound computation. We demonstrate our approach on three datasets: the public single person HumanEva dataset, outdoor sequences where multiple people interact in a group meeting scenario, and outdoor one-on-one basketball videos. The first dataset demonstrates that our framework achieves state-of-the-art performance in the single person setting, while the last two demonstrate robustness in the presence of partial and full occlusion and fast nontrivial motion.
Extremities, Probabilistic logic, Pattern analysis, Training, Graphical models, Search problems,pictorial structures, Tracking, motion
V. I. Morariu, D. Harwood, L. S. Davis, "Tracking People's Hands and Feet Using Mixed Network AND/OR Search", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. , pp. 1248-1262, May 2013, doi:10.1109/TPAMI.2012.187
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