Contour-motion feature (CMF): A space-time approach for robust pedestrian detection

  • Authors:

  • Yazhou Liu;Xilin Chen;Hongxun Yao;Xinyi Cui;Chaoran Liu;Wen Gao

  • Affiliations:

  • School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, PR China;Institute of Computing Technology, Chinese Academy of Science, Beijing 100080, PR China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, PR China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, PR China;Institute of Computing Technology, Chinese Academy of Science, Beijing 100080, PR China;Institute of Computing Technology, Chinese Academy of Science, Beijing 100080, PR China

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2009

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Abstract

This paper presents a contour-motion feature for robust pedestrian detection. The space-time contours are used as the low level representation of the pedestrian. Then we apply 3D distance transform to extend the 1-dimensional contour into 3-dimensional space. By this way, the relations between the local contours can be maintained implicitly. Further, by encapsulating the static and dynamic information by 3D Haar-like filters, we can generate the middle level pedestrian representation: contour-motion features. Then we use boosting method to select the most representative features. Our experiments demonstrate that the proposed approach can outperform Viola's well-known pedestrian detector in both detection accuracy and generalization ability. In addition, even though our approach is presented in pedestrian detection scenario, it has been extended to human activity recognition application and remarkable performance has been achieved.