Structural Regression Model Based Inverse Sparse Representation for Tracking Objects

Abstract

In order to reduce the calculation cost and improve the robustness of appearance model, this paper presents an optimal object tracking method that consists of improved inverse sparse representation and global spatial envelope. First, partial least squares regression-based structural model is adopted, which easily facilitates target template sparsely represented by candidate dictionary. Furthermore, candidates with nonzero coefficients are easily selected as possible tracking results. Meanwhile, partial occlusion and slight appearance changes are effectively alleviated during the tracking process. Second, spatial envelope in the frequency domain is utilized to select the best candidate from the inverse sparse representation process. Multiple scales and orientations-based Gabor filters are established to obtain the Gist information, which keeps the potential structural attributes of local appearance models to tolerate appearance variation. In addition, the Bayesian inference framework is used to exploit candidate samples, and a simple model update scheme is employed to alleviate drifting caused by temporal varying multi-factors. The qualitative experimental results show that the proposed tracking algorithm provides a better performance in some dynamic scenes.