LAUSR

Most existing trackers perform well in the occurrence of short-term occlusions and appearance and illumination variations, but struggle with the challenges of long-term tracking which include heavy or long-term occlusions, and out-of-view objects. We propose a spatial-temporal aware long-term object tracking method in this paper, to manage the challenges of long term tracking. Firstly, we present a spatial-temporal aware correlation filter, which jointly models the spatial and temporal information of the target within the correlation filter framework to locate a visible target from frame to frame. Then, we design a tracking uncertainty detection mechanism to activate the re-detector in case of track failure. The mechanism relies on the variations of correlation response measured spatially, and the appearance similarity estimated temporally. Finally, we propose a spatial-temporal aware long-term re-detector to recover the target when it becomes visible. In the re-detection process, a large number of candidates are sampled, evaluated, and refined spatially to obtain accurate locations. Additionally, reliable memory retained through conservative template updating enables the recovery of the target by similarity matching. The spatial-temporal information is explicitly encoded in each component, which operates collaboratively to boost the overall performance. Extensive experimental results conducted on publicly available benchmark datasets demonstrate that the proposed method performs favorably when compared to other state-of-the-art trackers.