Separating occluded humans by bayesian pixel classifier with re-weighted posterior probability

Authors:
Daehwan Kim;Yeonho Kim;Daijin Kim
Affiliations:
Department of Computer Science and Engineering, Pohang University of Science and Technology, Nam-Gu, Pohang, Korea;Department of Computer Science and Engineering, Pohang University of Science and Technology, Nam-Gu, Pohang, Korea;Department of Computer Science and Engineering, Pohang University of Science and Technology, Nam-Gu, Pohang, Korea
Venue:
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Year:
2011

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Abstract

This paper proposes a Bayesian pixel classification method with re-weighted posterior probability for separating multiple occluded humans. We separate the occluded humans by considering the occlusion region as a pixel classification problem. First, we detect an isolated human using the human detector. Then we divide it into three body parts (head, torso, and legs) using the body part detector, and model the color distributions of each body part using a naive Bayes classifier. Next, we detect an occlusion region by associating the occluded humans in consecutive frames. Finally, we identify the pixels associated with a human or body parts in occlusion region by the Bayesian pixel classifier with reweighted posterior probability, which can classify them more accurately. Experimental results show that our proposed method can classify pixels in an occlusion region and separate multiple occluded humans.