High Speed and Robust RGB-Thermal Tracking via Dual Attentive Stream Siamese Network

To meet the need of robust tracking in some cases (e.g., extremely illumination and thermal crossover), plenty of RGB-T tracking methods have been proposed in recent years. However, many of them can hardly meet the real-time standard since they are difficult to balance the robust-ness and the speed. To address such problems, we propose a new Dual Attentive Siamese Network (DASN) for RGB-T tracking. Specifically, we use a dual-stream siamese deep learning network to model tracking as a similarity measure task. In addition, to promote the information propagation procedure between two modalities and suppress the inter-ference of background, we design the channel attention module (CAFE Module) and the channel-spatial attention module (CSAFE Module). What's more, the dual modality region proposal sub-network and the strategy of selecting proposal are constructed to boost the performance. The proposed DASN is trained end-to-end offline. Extensive ex-periments on three real RGB-T tracking datasets show that our tracker achieves very competive results with a high tracking speed over 140 frames per second. Code is released at https://github.com/easycodesniper-atk/SiamCSR.git.