Research on person re-identification based on multi-level attention model

Person Re-identification (ReID) is an important research direction in the field of pattern recognition, which aims to retrieve the same pedestrian in different cameras. The combination of deep learning and attention mechanism greatly improves the accuracy of image retrieval, but previous researchers usually use on-channel or spatial convolution to learn attention, ignoring the connection between attention feature nodes. In this article, we first improve a bottleneck attention module (BAM) to make the learned attention map faster. Secondly, to capture the relevance of each feature node in the global attentional feature map, we design a self-relevant attention module (SRA), which models the global scope structure information and is used to capture the connection between the feature node positions to make the obtained attentional map more robust. Finally, we propose a method to strengthen the attention features, so that the higher attention features around the position also get higher feature values, so that the obtained feature map is more robust. The effectiveness of the model is confirmed in several mainstream pedestrian re-identification datasets, and the proposed model outperforms most state-of-the-art methods.