ROBUST RGB-T TRACKING VIA CONSISTENCY REGULATED SCENE PERCEPTION

RGB-T tracking has received increasing attention due to its significant advantage under severe weather conditions. Existing RGB-T tracking methods pay close attention to the representation of target appearance, ignoring the importance of scene information. In this paper, we propose a global reasoning-oriented method for RGB-T tracking. In particular, within a multi-task learning framework, our approach adopts a nested global reasoning model to regulate the consistency of scene perception (reasoning the relation between targets and the surrounding semantic regions) in different image domains. Moreover, a meta-unsupervised learning strategy is designed to enforce the nested global reasoning model to utilize partial multi-domain target information for the updating of scene perception. Extensive experiments on GTOT, RGBT210 and LasHeR datasets show the superior performance of our method when compared with related works.