A Person Re-Identification Network Based upon Channel Attention and Self-Attention

With the development of deep learning, person ReIdentification (Re-ID) technology has made great achievements. However, there are still some problems, such as pedestrian occlusion, different imaging conditions of posture changes, etc, which can lead to large changes in appearance, and it is difficult to obtain sufficient distinguishable features. Therefore, this paper proposes a network based on the fusion of channel attention mechanism and self-attention mechanism. The network learns more discriminative global features and local features from the spatial dimension and channel dimension. ResNet-50 is utilized as the backbone network. The channel attention module can capture the dependence of channel dimensions, obtain the weight of the importance of feature channels, and improve useful local feature to suppress useless feature. The self-attention module captures the context information from the spatial dimension to obtain the weight of each feature, and further obtains the global feature. Experiments on two datasets reveal the proposed model improves the accuracy compared with the state-of-the-art methods.