Fast object tracking using color histograms and patch differences

Dae Youn Lee, Jae Young Sim, Chang-Su Kim

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

1 Citation (Scopus)

Abstract

A fast visual object tracking algorithm using novel object appearance models is proposed in this work. We develop a color histogram model and a patch difference model to extract color and texture feature vectors, respectively. Then, we apply k-nearest neighbor classifiers to the color and texture feature vectors and obtain the foreground probability map. We then perform a hierarchical mean shift process on the map to identify the object window. Experimental results demonstrate that proposed algorithm outperforms the conventional algorithms in terms of both tracking accuracy and processing speed.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages3905-3908
Number of pages4
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 2013 Sep 152013 Sep 18

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Other

Other2013 20th IEEE International Conference on Image Processing, ICIP 2013
CountryAustralia
CityMelbourne, VIC
Period13/9/1513/9/18

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Keywords

  • Object tracking
  • appearance model
  • k-nearest neighbor
  • mean shift localization
  • tracking-by-detection

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Lee, D. Y., Sim, J. Y., & Kim, C-S. (2013). Fast object tracking using color histograms and patch differences. In 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings (pp. 3905-3908). [6738804] (2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings). https://doi.org/10.1109/ICIP.2013.6738804