Crowd counting and segmentation in visual surveillance

Authors:
Lu Wang;Nelson H. C. Yung
Affiliations:
Department of Electrical and Electronic engineering, The University of Hong Kong;Department of Electrical and Electronic engineering, The University of Hong Kong
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

In this paper, the crowd counting and segmentation problem is formulated as a maximum a posterior problem, in which 3D human shape models are designed and matched with image evidence provided by foreground/background separation and probability of boundary. The solution is obtained by considering only the human candidates that are possible to be un-occluded in each iteration, and then applying on them a validation and rejection strategy based on minimum description length. The merit of the proposed optimization procedure is that its computational cost is much smaller than that of the global optimization methods while its performance is comparable to them. The approach is shown to be robust with respect to severe partial occlusions.