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 re-weighted 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.
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© 2011 Springer-Verlag Berlin Heidelberg
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Kim, D., Kim, Y., Kim, D. (2011). Separating Occluded Humans by Bayesian Pixel Classifier with Re-weighted Posterior Probability. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2011. Lecture Notes in Computer Science, vol 6915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23687-7_49
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DOI: https://doi.org/10.1007/978-3-642-23687-7_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23686-0
Online ISBN: 978-3-642-23687-7
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