Learning Multi-Scale Features and Batch-Normalized Global Features for Person Re-Identification

Abstract

In recent years, person re-identification based on deep learning approaches has made great progress and achieved good results. However, many of the latest network design methods, which usually deploy ResNet or SENet as the backbone network, were originally designed for classification tasks. Since the person re-identification task is essentially different from the classification task, the structure of the backbone network should be modified accordingly. In this paper, we propose a retrieval network based on a multi-scale backbone architecture, which is specifically suitable for the person re-identification task. By constructing hierarchical residual-like connections within a single residual block, the model learns multi-scale discriminative features of pedestrian images. Unlike many state-of-the-art methods that use complex network structures and concatenate multi-branch features, our proposed retrieval network is implemented using only global features, simple triplet loss, and softmax with cross-entropy loss. The results of extensive experiments show that the proposed network has stronger fine-grained pedestrian representation ability, leading to performance gains for person re-identification tasks. Our proposed network achieves a rank-1 accuracy of 96.03% on the Market-1501 and 92.11% on DukeMTMC-reID datasets while only using global features.