Separable-programming based probabilistic-iteration and restriction-resolving correlation filter for robust real-time visual tracking.

Visual tracking methods based on correlation filter (CF) are to estimate the location and the scale of tracking object within video sequences. However, there are still several inadequate hypotheses within the CF framework. These inadequate hypotheses include the constant Gaussian label map, the predetermined object location and the insufficient utilization for features. To further address the problems caused by these hypotheses, this paper proposes a novel separable-programming based probabilistic-iteration and restriction-resolving correlation filter (PRCF). The main innovation points of this work are: 1) the separable-programming based tracking framework is adopted to achieve adaptive regulation incorporation and to simultaneously address location prediction and filter training; 2) the probabilistic-iteration scheme is suggested to update the Gaussian label for each frame to solve the problem of ad-hoc label map; 3) the adaptive feature fusion measure is introduced to abate the effects of insufficient utilization for numerous features; 4) the convergence behavior of PRCF is proved and discussed through theoretical analysis and worst-case convergence rate calculation. Experiments have also been conducted to test the efficiency of suggested PRCF on 7 benchmarks: OTB100, TC128, VOT2016, VOT2019, UAV123, NFS and LaSOT. The results have indicated that: 1) the PRCF obtains favorable performances compared with other 15 state-of-the-art (SOTA) CF-based trackers; 2) the PRCF reports comparable results against 4 deep learning based trackers; 3) the PRCF achieves a real-time speed of 52 frames-pre-second (FPS) on 7 benchmarks averagely. Thus, the PRCF is qualified for practical target tracking scenarios such as video surveillance and unmanned aerial vehicles (UAVs). [ABSTRACT FROM AUTHOR]