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

  • Tung-Ying Lee
  • Tsung-Yu Lin
  • Szu-Hao Huang
  • Shang-Hong Lai
  • Shang-Chih Hung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6523)

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 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tung-Ying Lee
    • 1
  • Tsung-Yu Lin
    • 1
  • Szu-Hao Huang
    • 1
  • Shang-Hong Lai
    • 1
  • Shang-Chih Hung
    • 2
  1. 1.Department of Computer ScienceNational Tsing Hua UniversityHsinChuTaiwan
  2. 2.Industrial Technology Research InstituteIdentification and Security Technology CenterTaiwan

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