Improving Rgb-Infrared Pedestrian Detection by Reducing Cross-Modality Redundancy
Qingwang Wang, Yongke Chi, Tao Shen, Jian Song, Zifeng Zhang, Yan Zhu
Abstract
Existing RGB-Infrared detection models do not explicitly encourage RGB and infrared to achieve effective multimodal learning. We find that when fusing RGB and infrared images, cross-modal redundant information weakens the degree of complementary information fusion. Inspired by this observation, we propose Redundant Information Suppression Network (RISNet) which suppresses cross-modal redundant information and facilitates the fusion of RGB-Infrared complementary information. Specifically, we design a novel mutual information minimization module to reduce the redundancy between appearance features from RGB images and infrared radiation features from infrared images, which enables the network to take full advantage of the complementary advantages of multimodality and improve the detection performance. Experimental results demonstrate that RISNet outperforms the best competitive algorithm for RGB-Infrared pedestrian detection.