CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.12 - Dec.
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,dictionary learning, Sparse representation, tracking, K-selection, appearance model
Baiyang Liu, Junzhou Huang, Casimir Kulikowski, Lin Yang, "Robust Visual Tracking Using Local Sparse Appearance Model and K-Selection", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 12, pp. 2968-2981, Dec. 2013, doi:10.1109/TPAMI.2012.215