Track Matching by Major Color Histograms Matching and Post-matching Integration

  • Eric Dahai Cheng
  • Massimo Piccardi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


In this paper we present a track matching algorithm based on the “major color” histograms matching and the post-matching integration useful for tracking a single object across multiple, limitedly disjoint cameras. First, the Major Color Spectrum Histogram (MCSH) is introduced to represent a moving object in a single frame by its most frequent colors only. Then, a two-directional similarity measurement based on the MCHS is used to measure the similarity of any two given moving objects in single frames. Finally, our track matching algorithm extends the single-frame matching along the objects’ tracks by a post-matching integration algorithm. Experimental results presented in this paper show the accuracy of the proposed track matching algorithm: the similarity of two tracks from the same moving objects has proved as high as 95%, while the similarity of two tracks from different moving objects has been kept as low as up to 28%. The post-matching integration step proves able to remove detailed errors occurring at the frame level, thus making track matching more robust and reliable.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Eric Dahai Cheng
    • 1
  • Massimo Piccardi
    • 1
  1. 1.Faculty of Information TechnologyUniversity of Technology, Sydney (UTS)BroadwayAustralia

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