Pedestrian Re-recognition Based on Hybrid Network

Abstract

With the rapid development of related computer vision algorithms, the large-scale use of video surveillance systems has not only improved traffic safety, but also promoted the development of intelligent high-speed. However, due to the complexity of the application scene, especially in the face of complex scene occlusion factors, the noise generated by the occlusion inevitably leads to the loss of the feature information of the identified person or object, which poses a great challenge to the existing pedestrian re-recognition algorithms. Therefore, this paper proposes a novel pedestrian re-recognition based on hybrid network. Feature extraction is carried out on four cooperative branches: local branch, global branch, global contrast pool branch and associated branch, and powerful diversity pedestrian feature expression ability is obtained. The network in this paper can be applied to different backbone networks. Through experimental comparison, the proposed algorithm has certain advantages compared with the latest methods, and the ablation experimental analysis further proves the effectiveness of the proposed network structure.