title: Robust visual tracking using local sparse appearance model and K-selection.
abstract: 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.
authors.affiliation: Rutgers University, Piscataway.
authors.forename: Baiyang
authors.initials: B
authors.lastname: Liu
authors.forename: Junzhou
authors.initials: J
authors.lastname: Huang
authors.forename: Casimir
authors.initials: C
authors.lastname: Kulikowski
authors.forename: Lin
authors.initials: L
authors.lastname: Yang