Real-Time Long-Term Tracking With Reliability Assessment And Object Recovery

In recent years, many visual tracking algorithms based on discriminative correlation filters have been proposed and proved to be successful in short-term tracking. However, most algorithms do not handle long-term tracking well due to factors such as occlusion and deformation. The aim of this paper is to propose a long-term tracking method with reliability assessment and object recovery. First, the relationship between the overlap rate and the response value is extensively studied, and then from the perspective of the time axis, the tracking process is evaluated by the fluctuation trend of the continuous response value. In the object recovery mechanism, we propose to alternately use local search and global search to improve the efficiency of detection. To this end, a sliding window is designed for cyclic shifting in the local region to achieve dense sampling within the region of interest, and EdgeBox is used in the global search to achieve target detection. Further, flexible switching between local search and global search is achieved by the difference in displacement of the object. Extensive experimental results on several benchmark datasets demonstrate that the proposed long-term tracker can achieve state-of-the-art accuracy with real-time speed of about 27 frames per second.