RSGNet: Relation based Skeleton Graph Network for Crowded Scenes Pose Estimation

Despite of the recent great progress on multi-person pose estimation, existing solutions still remain challenging under the condition of “crowded scenes”, where RGB images capture complex real-world scenes with highly-overlapped people, severe occlusions and diverse postures. In this work, we focus on two main problems: 1) how to design an effective pipeline for crowded scenes pose estimation; and 2) how to equip this pipeline with the ability of relation modeling for interference resolving. To tackle these problems, we propose a new pipeline named Relation based Skeleton Graph Network (RSGNet). Unlike existing works that directly predict joints-of-target by labeling joints-of-interference as false positive, we first encourage all joints to be predicted. And then, a Target-aware Relation Parser (TRP) is designed to model the relation over all predicted joints, resulting in a targetaware encoding. This new pipeline will largely relieve the confusion of the joints estimation model when seeing identical joints with totally distinct labels (e. g. , the identical hand exists in two bounding boxes). Furthermore, we introduce a Skeleton Graph Machine (SGM) to model the skeletonbased commonsense knowledge, aiming to estimate the target pose with the constraint of human body structure. Such skeleton-based constraint can help to deal with the challenges in crowded scenes from a reasoning perspective. Solid experiments on pose estimation benchmarks demonstrate that our method outperforms existing state-of-the-art methods. The code and pre-trained models are publicly available online1.

Authors

• Yan Dai
• Xuanhan Wang
• Lianli Gao
• Jingkuan Song University of Electronic Science and Technology of China
• Heng Tao Shen University of Electronic Science and Technology of China Tongji University

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