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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations


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.

Original languageEnglish
Title of host publicationAdvances in Multimedia Modeling - 17th International Multimedia Modeling Conference, MMM 2011, Proceedings
Number of pages10
EditionPART 1
StatePublished - 2011
Event17th Multimedia Modeling Conference, MMM 2011 - Taipei, Taiwan
Duration: 5 Jan 20117 Jan 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6523 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th Multimedia Modeling Conference, MMM 2011


  • Probabilistic occupancy map
  • human localization
  • multi-camera surveillance
  • video surveillance


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