Person Detection for Indoor Videosurveillance Using Spatio-temporal Integral Features

  • Adrien Descamps
  • Cyril Carincotte
  • Bernard Gosselin
Part of the Communications in Computer and Information Science book series (CCIS, volume 277)

Abstract

In this paper, we address the problem of person detection in indoor videosurveillance data. We present a new method based on the state of the art integral channel features. This approach is extended to allow the use of temporal features in addition to appearance based features. The temporal features are integrated by a robust background subtraction method. Our method is then evaluated on several datasets presenting various and challenging conditions typical of videosurveillance context. The evaluation shows that additional temporal features are efficient and improve greatly the performance of the detector.

Keywords

False Alarm Background Subtraction Integral Image Pedestrian Detection Channel Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Adrien Descamps
    • 1
  • Cyril Carincotte
    • 2
  • Bernard Gosselin
    • 1
  1. 1.TCTS LabUniversity of MonsMonsBelgium
  2. 2.Multitel ASBLMonsBelgium

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