Reciprocal interlayer-temporal discriminative target model for robust visual tracking

Most Siamese algorithms take little account of the information interaction between the target and search region, leading to tracking results that are often disturbed by the cumulative effect of target-like distractors between layers. In this paper, we propose a reciprocal interlayer-temporal discriminative target model for robust visual tracking. Firstly, an interlayer target-aware enhancement model is constructed, which establishes pixel-by-pixel correlation between the template and search region to achieve interlayer feature information interaction. This alleviates the cumulative error caused by the blindness of the target to search region during feature extraction, enhancing target perception. Secondly, to weaken the impact of interference, a temporal interference evaluation strategy is designed. It utilizes the interframe candidate propagation module to build associations among multi-candidates in the current frame and the previous frame. Then, the similar distractors are eliminated by using object inference constraint, so as to obtain a more accurate target location. Finally, we integrate the interlayer target-aware enhancement model and temporal interference evaluation strategy into the Siamese framework to achieve reciprocity for robust target tracking. Experimental results show that our tracking approach performs well, especially on seven benchmark datasets, including OTB-100, TC-128, DTB, UAV-123, VOT-2016, VOT-2018 and GOT-10k.