Improved object tracking using an adaptive colour model

Zezhi Chen, Andrew M. Wallace

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

We present the results of a study to exploit a multiple colour space model (CSM) and variable kernels for object tracking in video sequences. The basis of our work is the mean shift algorithm; for a moving target, we develop a procedure to adaptively change the CSM throughout a video sequence. The optional CSM components are ranked using a similarity distance within an inner (representing the object) and outer (representing the surrounding region) rectangle. Rather than use the standard, Epanechnikov kernel, we have also used a kernel weighted by the normalized Chamfer distance transform to improve the accuracy of target representation and localization, minimising the distance between the two distributions of foreground and background using the Bhattacharya coefficient. To define the target shape in the rectangular window, either regional segmentation or background-difference imaging, dependent on the nature of the video sequence, has been used. Experimental results show the improved tracking capability and versatility of the algorithm in comparison with results using fixed colour models and standard kernels. © Springer-Verlag Berlin Heidelberg 2007.

Original languageEnglish
Title of host publicationEnergy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings
Pages280-294
Number of pages15
Volume4679 LNCS
Publication statusPublished - 2007
Event6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition - Ezhou, China
Duration: 27 Aug 200729 Aug 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4679 LNCS
ISSN (Print)0302-9743

Conference

Conference6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Abbreviated titleEMMCVPR 2007
CountryChina
CityEzhou
Period27/08/0729/08/07

Keywords

  • Adaptive colour space models
  • Object tracking
  • Video sequences

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  • Cite this

    Chen, Z., & Wallace, A. M. (2007). Improved object tracking using an adaptive colour model. In Energy Minimization Methods in Computer Vision and Pattern Recognition - 6th International Conference, EMMCVPR, 2007 Proceedings (Vol. 4679 LNCS, pp. 280-294). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4679 LNCS).