Focusing on shared areas for partial person re-identification

Person re-identification (Re-ID) can achieve ideal performance based on the prerequisite that the sampling image is complete. However, the whole body cannot be detected because pedestrians may be occluded or are at the edge of the surveillance range in real-world scenarios. Consequently, the image only contains part of the visible information of the pedestrian. When using the standard person re-identification to match the partial image with the complete one, we witness the problem of spatial misalignment and interference caused by missing areas. Hence, we propose a focused shared area model (FSA) for partial re-identification to solve such descriptive problems. We use self-supervised learning to locate the shared area and learn region-level features. In addition, we adopt self-attention mechanism to help the network visualize the important features of the image, thus reducing the influence of the background information. Finally, we verify the effectiveness of our method through experiments on two mainstream datasets: Market-1501, DukeMTMC-reID and two important partial datasets: Partial-REID and Partial-iLIDS.