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People in Seats Counting via Seat Detection for Meeting Surveillance

  • Hongyu Liang
  • Jinchen Wu
  • Kaiqi Huang
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)

Abstract

People in seats counting is very important for meeting surveillance. While as a canonical pattern recognition problem, it’s very difficult due to various appearances of people and other outliers such as bags and clothes. To solve this problem we propose a coarse-to-fine framework. Firstly, we use the coarse classification module to retrieve the completely empty seats. To overcome the influence of noises caused by shadows and light spots, we fuse multiple global features calculated by background subtraction. Then in the fine classification module, a proposed SW-HOG feature and the LBP feature are combined together to solve the problem of occlusion and make sure the classification is real time. Finally a time-related calibration module is applied to suppress some outliers across frames with condition that the video frames are not successive. Experiments on a real meeting dataset demonstrate that the accuracy of the proposed method reaches 99.88%.

Keywords

people in seats counting meeting surveillance coarse-to-fine classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hongyu Liang
    • 1
  • Jinchen Wu
    • 1
  • Kaiqi Huang
    • 1
  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of ScienceChina

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