RGB-T Object Tracking Based on Cross-channel Local Response Normalization
Peng Huang, Zhen Yang, Zhijian Yin · 2022
Visible images are sensitive to light and are easily affected by imaging technology, while infrared images are not affected by illumination and have strong penetration in rain or smog, so the combination of infrared and visible images in the object tracking can significantly improve tracking performance. In this paper, we propose an RGB-T tracking method based on the cross-channel normalization of local response and incorporate some improvements to the ADRNet framework. On the one hand, we improve the structure of the backbone network to obtain richer semantic information and more robust features, and the dropout layer is added to prevent overfitting. On the other hand, we use 3D average pooling to process the feature data convolved with cross-channel operations in the part of local response normalization to extract the timing information between video frames. We conducted a large number of experiments on the corresponding dataset and compared our tracker with some of the most advanced trackers. The results show that our tracking algorithm can achieve better performance on RGB-T object tracking.