Multi-person pose estimation is fundamental to many computer vision tasks and has made significant progress in recent years. However, few previous methods explored the problem of pose estimation in crowded scenes while it remains challenging and inevitable in many scenarios. Moreover, current benchmarks cannot provide an appropriate evaluation for such cases. In this paper, we propose a novel and efficient method to tackle the problem of pose estimation in the crowd and a new dataset to better evaluate algorithms. Our model consists of two key components: joint-candidate single person pose estimation (SPPE) and global maximum joints association. With multi-peak prediction for each joint and global association using graph model, our method is robust to inevitable interference in crowded scenes and very efficient in inference. The proposed method surpasses the state-of-the-art methods on CrowdPose dataset by 4.8 mAP and results on MSCOCO dataset demonstrate the generalization ability of our method. Source code and dataset will be made publicly available.

本研究提出一种新颖的、高效的多人姿态估计方法，通过图模型的全局关键点最大化关联和每个关节的多峰预测，实现在拥挤场景下的准确姿态估计和高效推理，并提供新的数据集以更好地评估算法。在CrowdPose数据集上，本方法的mAP优于最先进的方法5.2，并在MSCOCO数据集上验证了本方法的泛化能力。

CrowdPose：高效的拥挤场景姿态估计和新基准