Person re-identification method based on multi-scale residual pooling

Person re-identification is a cross camera pedestrian retrieval problem. The data retrieved by pedestrians can be images, videos, and text. The current person re-identification methods are insufficient in expression of pedestrian features and poor robustness, resulting in low model accuracy. This paper proposes a Multi-scale Residual Pooling for person re-identification. ResNet50 is used as the basic network to obtain the multi-scale features. Global average pooling and maximum average pooling are performed on the input features at different network levels. Each group of average pooled and maximum pooled features is subtracted to remove the influence of image background clutter. The subtracted difference features are added to the maximum pooled features to obtain a more discriminative residual pooled fusion feature, making the network focus on the whole body contour of pedestrians and the difference between pedestrians and background. On this basis, triplet loss and cross-entropy loss are combined to optimize the model, and reordering technology is used to optimize the network. The experimental results showed that the Rank1 index of this paper’s method tested on the Market1501 and Duke MTMC-reID datasets reaches 96.41% and 91.43%, respectively, and mAP (mean Average Precision) reaches 94.52% and 89.30%, respectively. which is better than the current mainstream algorithms.