Robust tracking using local sparse appearance model and K-selection

  • Authors:

  • Baiyang Liu; Junzhou Huang; Lin Yang;C. Kulikowsk

  • Affiliations:

  • Rutgers, State Univ. of New Jersey, Piscataway, NJ, USA;Rutgers, State Univ. of New Jersey, Piscataway, NJ, USA;Robert Wood Johnson Med. Sch., UMDNJ, Piscataway, NJ, USA;Rutgers, State Univ. of New Jersey, Piscataway, NJ, USA

  • Venue:
  • CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
  • Year:
  • 2011

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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.