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 balancing 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.