Pedestrian re-identification based on attribute mining and reasoning

The high-level semantic information extracted from the pedestrian attribute feature is an important element for pedestrian recognition. Pedestrian attribute recognition plays an important role in both intelligent video surveillance and pedestrian re-identification promoting the convenience of searching and performance of model. This paper tries finding a practical method to improve the performance of the pedestrian re-identification by combining pedestrian attributes and identities. The multi-task learning method combines pedestrian recognition and attribute information in a direct way that considers the correlation between pedestrian attributes and identities but ignores the principle and degree of such correlation. To solve this problem, a new pedestrian recognition framework based on attribute mining and reasoning is proposed in this paper. To enhance the expression ability of attribute features, it designs spatial channel attention module (SCAM) based on attention mechanism to extract features from every attribute. SCAM can not only locate the attributes on the feature map, but also effectively mine channel features with a higher degree of association with attributes. In addition, both spatial attention model and channel attention model are integrated by multiple groups of parallel branches, which further improve the network performance. Finally, using the semantic reasoning and information transmission function of graph convolutional network, the relationship between attribute features and pedestrian features can be mined. Besides, pedestrian features with stronger expression ability can also be obtained. Experiment work is conducted in two databases, DukeMTMC-reID and Market-1501, which are commonly used in pedestrian recognition tasks. On the Market-1501 dataset, the final effect of the algorithm model CMC-1 can reach 94.74%, and mAP can reach 87.02%; on the DukeMTMC-reID dataset, CMC-1 can reach 87.03%, and mAP can reach 77.11%. The results show that our method is at the top of the existing pedestrian recognition methods.