2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-7.2
Paper Title PARTITIONED CENTERPOSE NETWORK FOR BOTTOM-UP MULTI-PERSON POSE ESTIMATION
Authors Jiahua Wu, Hyo Jong Lee, Jeonbuk National University, Republic of Korea
SessionMLR-APPL-IP-7: Machine learning for image processing 7
LocationArea E
Session Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In bottom-up multi-person pose estimation method, grouping joint candidates into corresponding person instance is a challenging problem. In this paper, a new bottom-up method, Partitioned CenterPose (PCP) Network, is proposed to better cluster all detected joints. To achieve this goal, a novel Partition Pose Representation (PPR) is proposed which integrate person instance and body joint by joint offset. PPR leverages the center of human body and the offset between center point and body joint to encode human pose. To better enhance the relationship of body joints, we divide human body into five parts, and generate sub-PPR in each part. Based on PPR, PCP Network can detect persons and body joints simultaneously, and then grouping all body joints by joint offset. Moreover, an improved l1 loss is designed to obtain more accurate joint offset. On the COCO keypoints dataset, the proposed method performs on par with the existing state-of-the-art bottom-up method in accuracy and speed.