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Article

3D Pose Detection of Closely Interactive Humans Using Multi-View Cameras

by 1,2,†, 1,2,†, 2, 2,* and 2
1
Graduate school at Shenzhen, Tsinghua University, Shenzhen 518055, China
2
Department of Automation, Tsinghua University, Beijing 100091, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(12), 2831; https://doi.org/10.3390/s19122831
Received: 29 April 2019 / Revised: 7 June 2019 / Accepted: 19 June 2019 / Published: 25 June 2019
(This article belongs to the Special Issue Multi-Modal Sensors for Human Behavior Monitoring)
We propose a method to automatically detect 3D poses of closely interactive humans from sparse multi-view images at one time instance. It is a challenging problem due to the strong partial occlusion and truncation between humans and no tracking process to provide priori poses information. To solve this problem, we first obtain 2D joints in every image using OpenPose and human semantic segmentation results from Mask R-CNN. With the 3D joints triangulated from multi-view 2D joints, a two-stage assembling method is proposed to select the correct 3D pose from thousands of pose seeds combined by joint semantic meanings. We further present a novel approach to minimize the interpenetration between human shapes with close interactions. Finally, we test our method on multi-view human-human interaction (MHHI) datasets. Experimental results demonstrate that our method achieves high visualized correct rate and outperforms the existing method in accuracy and real-time capability. View Full-Text
Keywords: human 3D pose; closely interactive; two-stage assembling; multi-view images human 3D pose; closely interactive; two-stage assembling; multi-view images
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MDPI and ACS Style

Li, X.; Fan, Z.; Liu, Y.; Li, Y.; Dai, Q. 3D Pose Detection of Closely Interactive Humans Using Multi-View Cameras. Sensors 2019, 19, 2831. https://doi.org/10.3390/s19122831

AMA Style

Li X, Fan Z, Liu Y, Li Y, Dai Q. 3D Pose Detection of Closely Interactive Humans Using Multi-View Cameras. Sensors. 2019; 19(12):2831. https://doi.org/10.3390/s19122831

Chicago/Turabian Style

Li, Xiu, Zhen Fan, Yebin Liu, Yipeng Li, and Qionghai Dai. 2019. "3D Pose Detection of Closely Interactive Humans Using Multi-View Cameras" Sensors 19, no. 12: 2831. https://doi.org/10.3390/s19122831

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