Aligned Spatial-Temporal Memory Network for Thermal Infrared Target Tracking

Abstract

Thermal infrared (TIR) target tracking is susceptible to occlusion and similarity interference, which obviously affects the tracking results. To resolve this problem, we develop an Aligned Spatial-Temporal Memory network-based Tracking method (ASTMT) for the TIR target tracking task. Specifically, we model the scene information in the TIR target tracking scenario using the spatial-temporal memory network, which can effectively store the scene information and decrease the interference of similarity interference that is beneficial to the target. In addition, we use an aligned matching module to correct the parameters of the spatial-temporal memory network model, which can effectively alleviate the impact of occlusion on the target estimation, hence boosting the tracking accuracy even further. Through ablation study experiments, we have demonstrated that the spatial-temporal memory network and the aligned matching module in the proposed ASTMT tracker are exceptionally successful. Our ASTMT tracking method performs well on the PTB-TIR and LSOTB-TIR benchmarks contrasted with other tracking methods.