Multi-object detection and tracking by stereo vision

  • Authors:

  • Ling Cai;Lei He;Yiren Xu;Yuming Zhao;Xin Yang

  • Affiliations:

  • The Department of Automation, Shanghai Jiao Tong University, Shanghai 200340, China;The National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA;The Department of Automation, Shanghai Jiao Tong University, Shanghai 200340, China;The Department of Automation, Shanghai Jiao Tong University, Shanghai 200340, China;The Department of Automation, Shanghai Jiao Tong University, Shanghai 200340, China

  • Venue:
  • Pattern Recognition
  • Year:
  • 2010

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Abstract

This paper presents a new stereo vision-based model for multi-object detection and tracking in surveillance systems. Unlike most existing monocular camera-based systems, a stereo vision system is constructed in our model to overcome the problems of illumination variation, shadow interference, and object occlusion. In each frame, a sparse set of feature points are identified in the camera coordinate system, and then projected to the 2D ground plane. A kernel-based clustering algorithm is proposed to group the projected points according to their height values and locations on the plane. By producing clusters, the number, position, and orientation of objects in the surveillance scene can be determined for online multi-object detection and tracking. Experiments on both indoor and outdoor applications with complex scenes show the advantages of the proposed system.