Robust tracking using local sparse appearance model and K-selection

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 co...

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Bibliographic Details
Main Authors: Baiyang Liu, Junzhou Huang, Lin Yang, Kulikowsk, C.
Format: Conference Proceeding
Language:English
Subjects:
Dictionaries
Encoding
Heuristic algorithms
Histograms
Image reconstruction
Libraries
Target tracking
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Summary: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.
ISSN:1063-6919
DOI:10.1109/CVPR.2011.5995730