Chapter

Advances in Multimedia Modeling

Volume 6523 of the series Lecture Notes in Computer Science pp 151-160

People Localization in a Camera Network Combining Background Subtraction and Scene-Aware Human Detection

  • Tung-Ying LeeAffiliated withDepartment of Computer Science, National Tsing Hua University
  • , Tsung-Yu LinAffiliated withDepartment of Computer Science, National Tsing Hua University
  • , Szu-Hao HuangAffiliated withDepartment of Computer Science, National Tsing Hua University
  • , Shang-Hong LaiAffiliated withDepartment of Computer Science, National Tsing Hua University
  • , Shang-Chih HungAffiliated withIndustrial Technology Research Institute, Identification and Security Technology Center

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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.

Keywords

Probabilistic occupancy map video surveillance human localization multi-camera surveillance