Person Re-identification Based on Multi-scale Network Attention Fusion

Abstract

The key to person re-identification depends on the extraction of pedestrian characteristics. Convolutional neural networks have powerful feature extraction and expression capabilities. In view of the fact that different features can be observed at different scales, a pedestrian re-identification method based on Multi-Scale Attention Network(MSAN) fusion is proposed. This method samples the features at different depths of the network and fuses the sampled features to predict pedestrians. Feature maps of different depths have different expressive powers, enabling the network to learn more fine-grained features of pedestrians. At the same time, the attention module is embedded in the residual network, so that the network can pay more attention to some key information and enhance the network feature learning ability. The accuracy of the proposed method on the datasets such as Market1501, DukeMTMC-reID and MSMT17_V1 reaches 95.3%, 89.8% and 82.2%, respectively. Experiments show that the method makes full use of the information of different depths of the network and the key information of interest, so that the model has strong discriminating ability, and the average accuracy of the proposed model is better than most state-of-the-art algorithms.