Background modeling by subspace learning on spatio-temporal patches

  • Authors:

  • Youdong Zhao;Haifeng Gong;Yunde Jia;Song-Chun Zhu

  • Affiliations:

  • Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing 100081, PR China and Lotus Hill Research Institute, EZhou 436000, PR ...;Lotus Hill Research Institute, EZhou 436000, PR China and Department of Statistics, UCLA, Los Angeles, CA 90095, Unites States and Google Inc., Mountain View, CA 94043, United States;Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing 100081, PR China;Lotus Hill Research Institute, EZhou 436000, PR China and Department of Statistics, UCLA, Los Angeles, CA 90095, Unites States

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2012

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Abstract

This paper presents a novel background model for video surveillance-Spatio-Temporal Patch based Background Modeling (STPBM). We use spatio-temporal patches, called bricks, to characterize both the appearance and motion information. Our method is based on the observation that all the background bricks at a given location under all possible lighting conditions lie in a low dimensional background subspace, while bricks with moving foreground are widely distributed outside. An efficient online subspace learning method is presented to capture the subspace, which is able to model the illumination changes more robustly than traditional pixel-wise or block-wise methods. Experimental results demonstrate that the proposed method is insensitive to drastic illumination changes yet capable of detecting dim foreground objects under low contrast. Moreover, it outperforms the state-of-the-art in various challenging scenes with illumination changes.