A multi-branch attention and alignment network for person re-identification

Person re-identification plays a critical role in video surveillance and has a variety of applications. However, the body misalignment caused by detectors or pose changes sometimes makes it challenging to match features extracted from different images. To address the issues above, we propose a multi-branch attention and alignment network (MAAN). This approach is based on a deep network with three main branches. One branch is used for global feature representations. Another branch implements a multi-attention process based on keypoints, filters the practical information in the image, and then horizontally partitions the image to extract local features. For the last branch, we create a method based on part feature alignment. We obtain 17 keypoints from a pretrained pose estimation model, and nine local regions from the corresponding feature map are extracted for alignment. Experimental results on various popular datasets demonstrate that our method can produce competitive results under posture changes and body misalignment.