Issue No. 12 - Dec. (2013 vol. 35)
Baiyang Liu , Dept. of Comput. Sci., Rutgers, State Univ. of New Jersey, Piscataway, NJ, USA
Junzhou Huang , Dept. of Comput. Sci. & Eng., Univ. of Arlington, Arlington, TX, USA
Casimir Kulikowski , Dept. of Comput. Sci., Rutgers, State Univ. of New Jersey, Piscataway, NJ, USA
Lin Yang , Dept. of Biostat., Univ. of Kentucky, Lexington, KY, USA
Online learned tracking is widely used for its 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 the stability and flexibility requirements. We have developed a robust tracking algorithm using a local sparse appearance model (SPT) and K-Selection. A static sparse dictionary and a dynamically updated online dictionary basis distribution are used to model the target appearance. A novel sparse representation-based voting map and a sparse constraint regularized mean shift are proposed to track the object robustly. Besides these contributions, we also introduce a new selection-based 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.
Visualization, Target tracking, Histograms, Adaptation models, Heuristic algorithms, Encoding
Baiyang Liu, Junzhou Huang, C. Kulikowski and Lin Yang, "Robust Visual Tracking Using Local Sparse Appearance Model and K-Selection," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. 12, pp. 2968-2981, 2013.