Occluded person re-identification based on differential attention siamese network

Occluded person re-identification (ReID) aims to retrieve images of the same person with occlusion. The key is excavating discriminative features from the nonoccluded regions to achieve better matching. To this end, we propose a novel Differential Attention Siamese Network (DASN) for occluded person ReID. Specifically, the proposed DASN method includes a siamese network module and a Differential Occlusion Weighted Aggregation (DOWA) module. First, a shared two-branch framework with the original nonoccluded image and the corresponding occluded image generated by the random erasing operation as inputs is adopted for shared feature representation. Then, the DOWA module is proposed to obtain the difference features in nonoccluded regions by first subtracting the feature map of the occluded image from the original image to obtain features corresponding to the occluded regions, and subsequently subtracting these features from those corresponding to the original image. During this process, the attention mechanism is integrated to alleviate the exploration inaccuracy of the features of nonoccluded areas. In the training process, multi-dimensional loss functions are integrated as the mentor for more effective learning supervision. Finally, an occluded person ReID model with better feature representation capability is produced. Extensive experiments are conducted on five publicly released databases for ReID. The results demonstrate that the proposed method outperforms the state-of-the-art methods in the occluded person ReID task and another two ReID tasks.