Abstract:Given the unbalanced pedestrian attribute data, insufficient expression ability of pedestrian features, and poor robustness of current pedestrian attribute recognition methods, this study proposes a method based on local feature overlap and pedestrian attribute recognition. The network uses global and local branches to improve the overall feature expression ability of the network. In the local branch, the feature graph obtained is divided into several parts with the same size, and the loss of each attribute is calculated with the Focal loss function to solve the problem of pedestrian attribute imbalance. Finally, the optimal loss of each attribute selected by voting and the ID loss calculated through global features are taken as the total loss of the model. The proposed method is tested on Market-1501_attribute and DukeMTMC-attribute pedestrian attribute datasets, and the experimental results show that this method can effectively improve the accuracy of pedestrian re-recognition.