Robust visual tracker combining temporal consistent constraint and adaptive spatial regularization

摘要

Existing discriminative correlation filters suffer from the defects of potential spatial distractors and the degradation of appearance model caused by hard-temporal correlation. Aiming at this issue, a robust tracker which combines the adaptive spatial regularization and the temporal consistent constraint is proposed in this paper. First, we propose to take the extracted saliency map of the background as a reference weight to construct the spatial regularization term, with which the perceived performance of the filter against distractors is enhanced by learning the spatial sparse constraint adaptively. Second, we further implement the temporal consistent regularization formed by capturing dynamic appearance information from multiple historical frames with a high-confidence strategy to mitigate the model degradation. Third, we employ the alternating direction method of multipliers to solve the constrained optimization problem efficiently, thereby the computational complexity can be reduced. The concrete experimental results on OTB-2013, OTB-2015, Temple-Color-128 and VOT2016 benchmarks demonstrate that our tracker outperforms several state-of-the-art algorithms.