Publication

Abstract : Generally all sparsity based models yields tracking performance in impressive manner, however computational complexity and low estimation of scale and orientation changes of target is aggravated in motion blur and background clutter challenges. In this paper, proposed method is based on a framework which projects templates matrix in candidates space. By selecting and weighting sparse coefficients, DSS map with pooling method leads to choose best candidate for tracking and by scale and orientation adaptive mean shift tracking method we can estimate scale, orientation of target adaptively. The result shows that better accuracy in tracking and robust to above challenges.