Pose-attention: A novel baseline for person re-identification

This paper proposes a novel baseline for deep person ReID methods by introducing human pose-based attention mechanism. Benefiting from deep convolutional network, there has been great progress of person re-identification (ReID) in recent years, which aims at retrieving the same person identities from images captured by different cameras. Most of existing methods focus on designing complex network structures to achieve higher scores on public datasets, but few works pay attention to baseline design. A strong baseline is crucial in experiments and could make the elaborated proposed methods more convincing. The present study makes use of a pre-trained human pose estimator to extract human key-point information. Then, we propose a novel manner to fuse pose information with global feature from Resnet50, which could lead the network concentrate more on discriminative key-point feature areas. Our work could achieve 94.8% rank-1 accuracy & 87.4% mean average precision (mAP) on Market1501, and outperform all other existing baselines that only use Resnet50 to our best knowledge. What's more, experiment results also suggest that with the help of pose information, our work could naturally be robust against misalignment and occlusion problems.