Robust tracking using local sparse appearance model and K-selection

Baiyang Liu, Junzhou Huang, Lin Yang, Casimir Kulikowski

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

460 Scopus citations

Abstract

Online learned tracking is widely used for it's adaptive ability to handle appearance changes. However, it introduces potential drifting problems due to the accumulation of errors during the self-updating, especially for occluded scenarios. The recent literature demonstrates that appropriate combinations of trackers can help balance stability and flexibility requirements. We have developed a robust tracking algorithm using a local sparse appearance model (SPT). A static sparse dictionary and a dynamically online updated basis distribution model the target appearance. A novel sparse representation-based voting map and sparse constraint regularized mean-shift support the robust object tracking. Besides these contributions, we also introduce a new dictionary learning algorithm with a locally constrained sparse representation, called K-Selection. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than alternatives reported in the recent literature.

Original languageEnglish (US)
Title of host publication2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
PublisherIEEE Computer Society
Pages1313-1320
Number of pages8
ISBN (Print)9781457703942
DOIs
StatePublished - Jan 1 2011

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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