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  • Research Article
  • Open Access

Feature Classification for Robust Shape-Based Collaborative Tracking and Model Updating

EURASIP Journal on Image and Video Processing20082008:274349

  • Received: 14 November 2007
  • Accepted: 10 July 2008
  • Published:


A new collaborative tracking approach is introduced which takes advantage of classified features. The core of this tracker is a single tracker that is able to detect occlusions and classify features contributing in localizing the object. Features are classified in four classes: good, suspicious, malicious, and neutral. Good features are estimated to be parts of the object with a high degree of confidence. Suspicious ones have a lower, yet significantly high, degree of confidence to be a part of the object. Malicious features are estimated to be generated by clutter, while neutral features are characterized with not a sufficient level of uncertainty to be assigned to the tracked object. When there is no occlusion, the single tracker acts alone, and the feature classification module helps it to overcome distracters such as still objects or little clutter in the scene. When more than one desired moving objects bounding boxes are close enough, the collaborative tracker is activated and it exploits the advantages of the classified features to localize each object precisely as well as updating the objects shape models more precisely by assigning again the classified features to the objects. The experimental results show successful tracking compared with the collaborative tracker that does not use the classified features. Moreover, more precise updated object shape models will be shown.


  • Image Processing
  • Pattern Recognition
  • Computer Vision
  • Good Feature
  • Classified Feature

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Authors’ Affiliations

Department of Biophysical and Electronic Engineering, University of Genoa, Via All'Opera Pia 11a, 16145 Genoa, Italy


© M. Asadi et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.