RGBT Tracking via Multi-stage Matching Guidance and Context integration.

RGB and Thermal (RGBT) tracking is an important supplement of visual object tracking for its' unique practical and research value. However, due to the limitations of the RGBT camera, extra interferences are introduced in the data synchronously. Removing or alleviating these interferences is a crucial direction to improve the performance of the RGBT tracking task. For this problem, we propose the multi-stage matching guidance and context integration network (M2GCI) for RGBT tracking. M2GCI reorganizes the feature-encoding pipeline into two context-integrating stages that are responsible for processing primary and senior information respectively. Firstly, the primary features encoded from primary encoders are integrated by the spatially adaptive fusion strategy. Next, the senior encoders cooperating with axial external-attention redistribution strategy further extract senior features which are more reliable for target prediction. Extensive experiments on the RGBT234 and GTOT provide that the proposed M2GCI can achieve more precision and robust tracking under some difficult scenarios compared with the excellent methods. [ABSTRACT FROM AUTHOR]