Research on 3D human pose estimation technique based on multi-view information fusion

Hao Wang; Ming Liu; Liquan Dong; Lingqin Kong

doi:10.1117/12.2687187

29 November 2023 Research on 3D human pose estimation technique based on multi-view information fusion

Hao Wang, Ming Liu, Liquan Dong, Lingqin Kong

Proceedings Volume 12769, Optical Metrology and Inspection for Industrial Applications X; 127691C (2023) https://doi.org/10.1117/12.2687187
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

Traditional 3D human pose estimation algorithms are often influenced by the accuracy of 2D keypoint detection and camera calibration, and they struggle to handle low-resolution and occluded scenes. To address these challenges, we propose a multi-view 3D human pose estimation method that incorporates prior information. At the 2D pose extraction stage, we design a bottom-up detection network called HRPifPaf to achieve accurate human pose detection in low-resolution scenarios. It first constructs a high-resolution feature extraction module that combines features from different scales. Then, a joint prediction and association module combines confidence scores and scale factors with vector directions pointing to the main body parts of the joints. We also utilize a Kalman filter to optimize the final detection results. At the 3D pose synthesis stage, we propose a multi-camera parameter joint optimization calibration method that leverages prior information of the human skeleton to address challenges such as body occlusion and inaccurate camera intrinsic and extrinsic parameters, designing a comprehensive cost function based on reprojection error, and human body geometry constraints.

Citation Download Citation

Hao Wang, Ming Liu, Liquan Dong, and Lingqin Kong "Research on 3D human pose estimation technique based on multi-view information fusion", Proc. SPIE 12769, Optical Metrology and Inspection for Industrial Applications X, 127691C (29 November 2023); https://doi.org/10.1117/12.2687187

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