Robust Visual Object Tracking with Spatiotemporal Regularisation and Discriminative Occlusion Deformation

Spatiotemporal regularized Discriminative Correlation Filters (DCF) have been proposed recently for visual tracking, achieving state-of-the-art performance. However, the tracking performance of the online learning model used in this kind methods is highly dependent on the quality of the appearance feature of the target, and the target feature appearance could be heavily deformed due to the occlusion by other objects or the variations in their dynamic self-appearance. In this paper, we propose a new approach to mitigate these two kinds of appearance deformation. Firstly, we embed the occlusion perception block into the model update stage, then we adaptively adjust the model update according to the situation of occlusion. Secondly, we use the relatively stable colour statistics to deal with the appearance shape changes in large targets, and compute the histogram response scores as a complementary part of final correlation response. Extensive experiments are performed on four well-known datasets, i.e. OTB100, VOT-2018, UAV123, and TC128. The results show that the proposed approach outperforms the baseline DCF method, especially, on the TC128/UAV123 datasets, with a gain of over 4.05 %/2.43% in mean overlap precision. We will release our code at https://github.com/SYLan2019/STD0D.