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Multi-feature Graph-Based Object Tracking

  • Murtaza Taj
  • Emilio Maggio
  • Andrea Cavallaro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4122)

Abstract

We present an object detection and tracking algorithm that addresses the problem of multiple simultaneous targets tracking in real-world surveillance scenarios. The algorithm is based on color change detection and multi-feature graph matching. The change detector uses statistical information from each color channel to discriminate between foreground and background. Changes of global illumination, dark scenes, and cast shadows are dealt with a pre-processing and post-processing stage. Graph theory is used to find the best object paths across multiple frames using a set of weighted object features, namely color, position, direction and size. The effectiveness of the proposed algorithm and the improvements in accuracy and precision introduced by the use of multiple features are evaluated on the VACE dataset.

Keywords

Object Detection Graph Match Cast Shadow Global Illumination Shadow Detection 
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

  • Murtaza Taj
    • 1
  • Emilio Maggio
    • 1
  • Andrea Cavallaro
    • 1
  1. 1.Queen Mary, University of London, Mile End Road, London E1 4NS (United Kingdom) 

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