Inteval Spatio-Temporal Constraints and Pixel-Spatial Hierarchy Region Proposals for Abrupt Motion Tracking

The RPN-based Siamese tracker has achieved remarkable performance with real-time speed but suffers from a lack of robustness in complex motion tracking. Especially when the target comes into an abrupt motion scenario, the assumption of motion smoothness may be broken, which will further compromise the reliability of tracking results. Therefore, it is important to develop an adaptive tracker that can maintain robustness in complex motion scenarios. This paper proposes a novel tracking method based on the interval spatio-temporal constraints and a region proposal method over a pixel-spatial hierarchy. Firstly, to cope with the limitations of a fixed-constraint strategy for abrupt motion tracking, we propose a question-guided interval spatio-temporal constraint strategy. Based on the consideration of tracking status and the degree of penalty expansion, it enables the dynamic adjustment of the constraint weights, which ensures a match between response scores and true confidence values. Secondly, to guarantee the coverage of a target using candidate proposals in extreme motion scenarios, we propose a region proposal method over the pixel-spatial hierarchy. By combining visual common sense with reciprocal target-distractor information, our method implements a careful refinement of the primary proposals. Moreover, we introduce a discriminative-enhanced memory updater designed to ensure effective model adaptation. Comprehensive evaluations on five benchmark datasets: OTB100, UAV123, LaSOT, VOT2016, and VOT2018 demonstrate the superior performance of our proposed method in comparison to several state-of-the-art approaches.