CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark
It addresses the problem of accurate pose estimation in crowded environments for computer vision applications, representing a domain-specific advancement.
The paper tackles pose estimation in crowded scenes by proposing a novel method with joint-candidate SPPE and global maximum joints association, achieving a 5.2 mAP improvement over state-of-the-art on the new CrowdPose dataset.
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 5.2 mAP and results on MSCOCO dataset demonstrate the generalization ability of our method. Source code and dataset will be made publicly available.