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Multiple Vehicle Tracking in Surveillance Videos

  • Yun Zhai
  • Phillip Berkowitz
  • Andrew Miller
  • Khurram Shafique
  • Aniket Vartak
  • Brandyn White
  • Mubarak Shah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4122)

Abstract

In this paper, we present KNIGHT, a Windows-based stand-alone object detection, tracking and classification software, which is built upon Microsoft Windows technologies. The object detection component assumes stationary background settings and models background pixel values using Mixture of Gaussians. Gradient-based background subtraction is used to handle scenarios of sudden illumination change. Connected- component algorithm is applied to detected foreground pixels for finding object-level moving blobs. The foreground objects are further tracked based on a pixel-voting technique with the occlusion and entry/exit reasonings. Motion correspondences are established using the color, size, spatial and motion information of objects. We have proposed a texture-based descriptor to classify moving objects into two groups: vehicles and persons. In this component, feature descriptors are computed from image patches, which are partitioned by concentric squares. SVM is used to build the object classifier. The system has been used in the VACE-CLEAR evaluation forum for the vehicle tracking task. Corresponding system performance is presented in this paper.

Keywords

Support Vector Machine Object Detection Surveillance Video Image Patch Foreground Object 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Yun Zhai
    • 1
  • Phillip Berkowitz
    • 1
  • Andrew Miller
    • 1
  • Khurram Shafique
    • 1
  • Aniket Vartak
    • 1
  • Brandyn White
    • 1
  • Mubarak Shah
    • 1
  1. 1.Computer Vision Laboratory, School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, Florida 32826USA

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