Person Re-identification Based on Fusion of Shared Feature Branches

Based on improvement of the existing person re-identification network model and investigating person re-identification related problems on open datasets, a pedestrian re-identification method that incorporates shared feature branching as well as a fused attention mechanism is proposed. First, the benchmark network framework of pedestrian re-identification method is formed by image pre-processing method, generalized average pooling and loss function together, and BNNeck structure is added to optimize the problem of inconsistent multi-loss objectives before obtaining classification features. Second, the global feature branching structure is proposed to be assisted by shared feature branching, which can effectively assist global feature branching to obtain more distinguishable pedestrian features and the attention network is combined with person re-recognition using a network structure that fuses non-local modules with channel attention to fully utilize the feature information of pedestrian images. Finally, the performance improvement of the benchmark network with shared feature branching and the fused attention mechanism approach is demonstrated by experiments on the re-identification dataset.