Person re-identification (Re-ID) is an important but challenging task in video surveillance applications. In Re-ID tasks, pose is an extremely useful cue to identify a person, even from the back view. Therefore, pose-detection models may learn the features that are beneficial to the Re-ID task and improve the Re-ID performance by fusing the feature maps into the Re-ID model. Two key problems in integrating the pose cues are addressed in this study. One is how to reduce the noise caused by cross-domain datasets. The other is how to fuse the feature maps to better utilize high-level semantic pose cues. To address these two key problems, we first propose PA-Net by combining the pose attention stream and the global attention stream, where the global attention stream distinguishes persons with different global appearances, and the pose attention stream distinguishes persons with similar global appearance but different poses. Then, we present a pose attention stream that learns local features to reduce the noise in the pose cues caused by the cross-domain datasets and provide more semantic information for the Re-ID task. The effects of the proposed pose attention are demonstrated in an ablation study, and comparative experiments show that PA-Net achieves state-of-the-art performance.

人员重新识别（Re-ID）在视频监控应用中是一项重要但具有挑战性的任务。在Re-ID任务中，姿势是识别人员的极有用的线索，即使是从背面看也是如此。因此，姿势检测模型可以学习对Re-ID任务有益的特征，并通过将特征图融合到Re-ID模型中来提高Re-ID性能。这项研究解决了整合姿势提示的两个关键问题。一种是如何减少由跨域数据集引起的噪声。另一个是如何融合特征图以更好地利用高级语义姿势提示。为了解决这两个关键问题，我们首先提出通过结合姿势注意流和全局注意流来提出PA-Net，其中全局注意流区分具有不同全局外观的人，姿势注意流将具有相似全局外观但姿势不同的人区分开来。然后，我们提出一个姿势注意流，该姿势学习流学习局部特征以减少由跨域数据集引起的姿势提示中的噪声，并为Re-ID任务提供更多的语义信息。消融研究证明了拟议的姿势注意的效果，并且对比实验表明，PA-Net达到了最先进的性能。