Robust Object Tracking using Sparse based Representative Dictionary Learning

Inspired by the recent advances in sparse theory, we propose a new sparse based object tracking framework in this paper. In order to effectively detect the target object to be tracked, we formulate and present a new sparse based representative dictionary learning algorithm and candidate selection scheme for robust object tracking. Specifically, we have used Pearson's correlation based dictionary learning algorithm to engender a highly representative dictionary. This representative dictionary is then used for modeling an adaptive appearance model for the target, which is robust to challenges like partial occlusions, illumination, clutter scenes and pose changes. We also present a new Appearance Model Structural Similarity test algorithm for meticulously identifying the target from various candidates in the consecutive frames of video sequences. Results of extensive experimentations carried out using various publicly available datasets shows that our framework produces an exorbitant performance compared to many state-of-the-art works.