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http://dx.doi.org/10.3745/KTSDE.2022.11.10.419

Deep Learning-Based Outlier Detection and Correction for 3D Pose Estimation  

Ju, Chan-Yang (한양대학교 인공지능융합학과 바이오인공지능융합전공)
Park, Ji-Sung (한양대학교 인공지능융합학과 바이오인공지능융합전공)
Lee, Dong-Ho (한양대학교 인공지능융합학과 바이오인공지능융합전공)
Publication Information
KIPS Transactions on Software and Data Engineering / v.11, no.10, 2022 , pp. 419-426 More about this Journal
Abstract
In this paper, we propose a method to improve the accuracy of 3D human pose estimation model in various move motions. Existing human pose estimation models have some problems of jitter, inversion, swap, miss that cause miss coordinates when estimating human poses. These problems cause low accuracy of pose estimation models to detect exact coordinates of human poses. We propose a method that consists of detection and correction methods to handle with these problems. Deep learning-based outlier detection method detects outlier of human pose coordinates in move motion effectively and rule-based correction method corrects the outlier according to a simple rule. We have shown that the proposed method is effective in various motions with the experiments using 2D golf swing motion data and have shown the possibility of expansion from 2D to 3D coordinates.
Keywords
Human Pose Estimation; Pose Refinement; Deep Learning;
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