Robustly tracking objects via multi-task kernel dynamic sparse model

Recently, sparse representation has been successfully applied by some generative tracking methods. However, few methods consider the correlation between the representations of each particle in time domain and space domain. Additionally, most methods use the raw pixels as templates which can not well adapt to the sophisticated object changes. To solve these problems, we consider the sparse representation in kernel space and propose a multi-task kernel dynamic sparse tracking algorithm (MTKDST). As compared to previous methods, our method exploits the dependencies between particles in the space domain and the correlation of particle representation in the time domain to improve the tracking performance. Furthermore, we also adopt the multikernel fusion mechanism to utilize multiple complementary visual features (e.g., spatial color histogram and spatial gradient-orientation histogram) to enhance the robustness of the proposed method. The comprehensive experiments on several challenging image sequences demonstrate that the proposed method outperforms the state-of-the-art approaches in tracking accuracy.