Robust visual tracking via scale-aware localization and peak response strength

Existing regression-based deep trackers usually localize a target based on a response map, where the highest peak response corresponds to the predicted target location. Nevertheless, when the background distractors appear or the target scale changes frequently, the response map is prone to produce multiple sub-peak responses to interfere with model prediction. In this paper, we propose a robust online tracking method via Scale-Aware localization and Peak Response strength (SAPR), which can learn a discriminative model predictor to estimate a target state accurately. Specifically, to cope with large scale variations, we propose a Scale-Aware Localization (SAL) module to provide multi-scale response maps based on the scale pyramid scheme. Furthermore, to focus on the target response, we propose a simple yet effective Peak Response Strength (PRS) module to fuse the multi-scale response maps and the response maps generated by a correlation filter. According to the response map with the maximum classification score, the model predictor iteratively updates its filter weights for accurate target state estimation. Experimental results on three benchmark datasets, including OTB100, VOT2018 and LaSOT, demonstrate that the proposed SAPR accurately estimates the target state, achieving the favorable performance against several state-of-the-art trackers.