People localization in a camera network combining background subtraction and scene-aware human detection

Authors:
Tung-Ying Lee;Tsung-Yu Lin;Szu-Hao Huang;Shang-Hong Lai;Shang-Chih Hung
Affiliations:
Department of Computer Science, National Tsing Hua University, HsinChu, Taiwan;Department of Computer Science, National Tsing Hua University, HsinChu, Taiwan;Department of Computer Science, National Tsing Hua University, HsinChu, Taiwan;Department of Computer Science, National Tsing Hua University, HsinChu, Taiwan;Identification and Security Technology Center, Industrial Technology Research Institute, Taiwan
Venue:
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Year:
2011

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Abstract

In a network of cameras, people localization is an important issue. Traditional methods utilize camera calibration and combine results of background subtraction in different views to locate people in the three dimensional space. Previous methods usually solve the localization problem iteratively based on background subtraction results, and high-level image information is neglected. In order to fully exploit the image information, we suggest incorporating human detection into multi-camera video surveillance. We develop a novel method combining human detection and background subtraction for multi-camera human localization by using convex optimization. This convex optimization problem is independent of the image size. In fact, the problem size only depends on the number of interested locations in ground plane. Experimental results show this combination performs better than background subtraction-based methods and demonstrate the advantage of combining these two types of complementary information.