Multiple model adaptive visual tracking with correlation filters

Visual target tracking has many challenges such as robustness to occlusion, noise, drifts. Although robust algorithms have been proposed to solve these problems, the solutions are not appropriate for real time implementations. On the other hand, tracking methods based on correlations filters can be efficiently designed. To achieve robust tracking, the present work presents a multiple model visual tracking method to adapt to changes in the target appearance using a correlation filters based tracker. Moreover, an algorithm for managing update rates of the filters has been proposed. By using a tracker with multiple models, both infinitesimally small movements and abrupt changes can be handled simultaneously. Our experiments have demonstrated that the proposed strategy improves the tracking maintenance and accuracy significantly in benchmark datasets compared to its single model counterpart.