Abstract:  Due to differences in camera parameters, shooting environment, and angles for pedestrian photography, the accuracy of pedestrian recognition algorithms still needs to be improved. To this end, a pedestrian re recognition algorithm based on pedestrian semantic perception information and deep learning is proposed. Firstly, super-resolution reconstruction of pedestrian views enhances the detailed features of pedestrian views, extracts the overall feature values of pedestrians, and uses them to identify pedestrians with significant body differences. Secondly, the Semantic information of pedestrian images is perceived, and the feature values of pedestrian Semantic information are extracted according to the above results to identify pedestrians with the same or similar body shape. Then, the macroscopic feature values of the human body and the semantic perception information feature values in the pedestrian video are fused into a comprehensive feature value. Use the generated feature values to calculate the distance between them and the video feature values of different individuals, and identify massive character images. Finally, this article validated the performance of the algorithm in different datasets. The experimental results show that the language perception based pedestrian recognition algorithm has the highest mAP and rand-1 values.

Key words: deep learning, pedestrian re-identification, semantic perception