Compressive tracking using incremental LS-SVM

As the development of Artificial Intelligent, computer vision has became one of the most important elements of all the technologies which composed the AI system, especially robot. Object tracking plays a key role in computer vision. While, there still remain some unsolved problems when the target suffering occlusion, illumination, scale change and rotation. The proposed tracking algorithm obtain the appearance model using the theory of compressive sensing, A LS-SVM classifier if used to separate the positive templates from negative samples. Then, we design a hypergraph propagation method to capture the contextual information on samples in order to improve the tracking accuracy. Updating scheme makes the algorithm more adaptive. Experimental results have proved the effectiveness and robustness of the proposed tracker.