Edge-Aware and Relation Mining Network for RGBT Tracking

Abstract Recently, the fusion tracking of RGB and thermal infrared images (RGBT) hasreceived most research interest. However, existing RGBT tracking algorithmsbased on deep learning mainly perform modality weight allocation to highlighttarget information, but background clutter may limit the improvement of trackingperformance. To solve this problem, we propose a new edge-aware and rela-tion mining network for RGBT tracking. This network takes advantage of thecomplementary advantages of edge information and deep information to makeup for the defect that deep information is susceptible to background interfer-ence. Meanwhile, a cross-modality relation mining module is designed to explorethe node correlation score to highlight target state information and reduce thebackground noise. Also, Transformer is utilized for bidirectional modulation tocapture cross-modality dependencies. Finally, multi-branch supervised learningand decision-level fusion are performed to improve tracking accuracy and suc-cess rate further. Extensive experiments on three publicly available datasets showthat the proposed algorithm performs excellently.