Yunfeng Li

Yunfeng Li, Bo Wang, Jiuran Sun et al.

Vision camera and sonar are naturally complementary in the underwater environment. Combining the information from two modalities will promote better observation of underwater targets. However, this problem has not received sufficient attention in previous research. Therefore, this paper introduces a new challenging RGB-Sonar (RGB-S) tracking task and investigates how to achieve efficient tracking of an underwater target through the interaction of RGB and sonar modalities. Specifically, we first propose an RGBS50 benchmark dataset containing 50 sequences and more than 87000 high-quality annotated bounding boxes. Experimental results show that the RGBS50 benchmark poses a challenge to currently popular SOT trackers. Second, we propose an RGB-S tracker called SCANet, which includes a spatial cross-attention module (SCAM) consisting of a novel spatial cross-attention layer and two independent global integration modules. The spatial cross-attention is used to overcome the problem of spatial misalignment of between RGB and sonar images. Third, we propose a SOT data-based RGB-S simulation training method (SRST) to overcome the lack of RGB-S training datasets. It converts RGB images into sonar-like saliency images to construct pseudo-data pairs, enabling the model to learn the semantic structure of RGB-S-like data. Comprehensive experiments show that the proposed spatial cross-attention effectively achieves the interaction between RGB and sonar modalities and SCANet achieves state-of-the-art performance on the proposed benchmark. The code is available at https://github.com/LiYunfengLYF/RGBS50.

Yunfeng Li, Bo Wang, Ye Li

The main problem in RGB-T tracking is the correct and optimal merging of the cross-modal features of visible and thermal images. Some previous methods either do not fully exploit the potential of RGB and TIR information for channel and spatial feature fusion or lack a direct interaction between the template and the search area, which limits the model's ability to fully utilize the original semantic information of both modalities. To address these limitations, we investigate how to achieve a direct fusion of cross-modal channels and spatial features in RGB-T tracking and propose CSTNet. It uses the Vision Transformer (ViT) as the backbone and adds a Joint Spatial and Channel Fusion Module (JSCFM) and Spatial Fusion Module (SFM) integrated between the transformer blocks to facilitate cross-modal feature interaction. The JSCFM module achieves joint modeling of channel and multi-level spatial features. The SFM module includes a cross-attention-like architecture for cross modeling and joint learning of RGB and TIR features. Comprehensive experiments show that CSTNet achieves state-of-the-art performance. To enhance practicality, we retrain the model without JSCFM and SFM modules and use CSNet as the pretraining weight, and propose CSTNet-small, which achieves 50% speedup with an average decrease of 1-2% in SR and PR performance. CSTNet and CSTNet-small achieve real-time speeds of 21 fps and 33 fps on the Nvidia Jetson Xavier, meeting actual deployment requirements. Code is available at https://github.com/LiYunfengLYF/CSTNet.