Learning Adaptive Attribute-Driven Representation for Real-Time RGB-T Tracking

The development of a real-time and robust RGB-T tracker is an extremely challenging task because the tracked object may suffer from shared and specific challenges in RGB and thermal (T) modalities. In this work, we observe that the implicit attribute information can boost the model discriminability, and propose a novel attribute-driven representation network to improve the RGB-T tracking performance. First, according to appearance change in RGB-T tracking scenarios, we divide the major and special challenges into four typical attributes: extreme illumination, occlusion, motion blur, and thermal crossover. Second, we design an attribute-driven residual branch for each heterogeneous attribute to mine the attribute-specific property and therefore build a powerful residual representation for object modeling. Furthermore, we aggregate these representations in channel and pixel levels by using the proposed attribute ensemble network (AENet) to adaptively fit the attribute-agnostic tracking process. The AENet can effectively make aware of appearance change while suppressing the distractors. Finally, we conduct numerous experiments on three RGB-T tracking benchmarks to compare the proposed trackers with other state-of-the-art methods. Experimental results show that our tracker achieves very competitive results with a real-time tracking speed. Code will be available at https://github.com/zhang-pengyu/ADRNet.