Temporal Constraint Background-Aware Correlation Filter With Saliency Map

Correlation filter (CF) based trackers have recently drawn great attention in visual tracking community due to their impressive performance, and computational efficiency on benchmark datasets. However, the performance of most existing trackers using correlation filter is hampered by two aspects: i) Included background information in the selected rectangular target patch is considered as part of the target, and they are treated as important as the real target in training new filter model, it causes the filter easily drift when target shape changes dramatically. ii) Existing filters use a moving average operation with an empirical weight to update the filter model in each frame, such per frame adaptation constantly introduces new information of the target patch, but never consider the consistence of the historical information, and the newly obtained one, further increases the risk of drifting. This paper presents a new framework including saliency map, and a novel CF regression model. We reformulate the original optimization problem, and provide a closed form solution for multidimensional features which is solved efficiently using alternating direction method of multipliers (ADMM), and accelerated using Sherman-Morrison lemma, our algorithm as a new framework can be easily integrated into CF base trackers to boost their tracking performance. We perform comprehensive experiments on five benchmarks: OTB-2015, VOT2016, VOT2018, UAV123, and TempleColor-128. Results show that the proposed method performs favorably against lots of state-of-the-art methods with a speed close to real-time. Our method with deep features performs much better on all 5 datasets.