Title :
Robust tracking using local sparse appearance model and K-selection
Author :
Liu, Baiyang ; Huang, Junzhou ; Yang, Lin ; Kulikowsk, Casimir
Author_Institution :
Rutgers, State Univ. of New Jersey, Piscataway, NJ, USA
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.
Keywords :
image representation; learning (artificial intelligence); object tracking; sparse matrices; K-selection; SPT; dictionary learning algorithm; local sparse appearance model; online learned tracking; online updated basis distribution model; potential drifting problems; robust object tracking algorithm; sparse constraint regularized mean-shift; sparse representation-based voting map; Dictionaries; Encoding; Heuristic algorithms; Histograms; Image reconstruction; Libraries; Target tracking;
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
Conference_Location :
Providence, RI
Print_ISBN :
978-1-4577-0394-2
DOI :
10.1109/CVPR.2011.5995730