A Double Stream Person Re-Identification Method Based on Attention Mechanism and Multi-Scale Feature Fusion

Person re-identification mainly plays a role in multiple non-overlapping camera monitoring environments to determine whether the target person of interest that has appeared under a camera appears again under others. However, the image data, in the real scene, taken by the surveillance camera may be occluded or blurred, which increased the difficulty of identifying the pedestrian posturn and then lead to dramatically decrease the accuracy of recognition. To solve the above problems, we propose a dual branch multi-scale feature fusion network, which improves the expression ability of pedestrian features under partial occlusion by learning discriminative pedestrian features. By embedding the lightweight attention module into Residual neural network-50 (Resnet-50), the image sequence features of the channel dimension will be extracted, and the interference caused by the cluttered back ground information will be suppressed. In the training phase, the average pooling layer and the maximum pooling layer of different kernels and strides are utilized for different residual stages, and the mixed pooling strategy and mixed loss function of different kernels are designed. By comparison with the existing representative methods on Market1501, DukeMTMC reID and MTMS17 datasets, the experimental results show that the features extracted by the proposed method are more discriminative and with high recognition accuracy.