Probabilistic Adaptive Spatial-Temporal Regularized Correlation Filters for UAV Tracking

Most existing trackers based on spatial-temporal regularized correlation filters exploit response map variation to adapt regularization terms to object appearance changes automatically. However, these trackers ignore the high uncertainty of the response map when the object is occluded or similar objects around, making them unable to learn reliable filters accurately. Furthermore, most correlation filters use linear interpolation directly to update the filter model at each frame, which may cause model degradation once the tracking result is inaccurate or missing. In this work, we propose a novel probabilistic adaptive spatial-temporal regularized correlation filters (PASTRCF) to solve the two issues mentioned above. A probabilistic model constructing the reliability of the response map is introduced to accurately utilize the information in the response map to learn regularization coefficients adaptively. The adaptive threshold mechanism provides an appropriate strategy to update the filter model to alleviate model degradation. Extensive experiments on UAV benchmarks have proven the favorable performance of our method compared to the state-of-art trackers, with robust tracking while ensuring real-time performance.