ETRI-Knowledge Sharing Plaform

ENGLISH

성과물

논문 검색
구분 SCI
연도 ~ 키워드

상세정보

학술대회 Accurate and Efficient 3D Human Pose Estimation Algorithm using Single Depth Images for Pose Analysis in Golf
Cited 8 time in scopus Download 14 time Share share facebook twitter linkedin kakaostory
저자
박순찬, 장주용, 정혁, 이재호, 박지영
발행일
201707
출처
Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2017, pp.49-57
DOI
https://dx.doi.org/10.1109/CVPRW.2017.19
협약과제
17HS1200, 디지털라이프를 위한 비접촉식 사용자 상태·의도 인지기반의 지능형 인터랙션 기술 개발, 박지영
초록
Human pose analysis has been known to be an effective means to evaluate athlete's performance. Marker-less 3D human pose estimation is one of the most practical methods to acquire human pose but lacks sufficient accuracy required to achieve precise performance analysis for sports. In this paper, we propose a human pose estimation algorithm that utilizes multiple types of random forests to enhance results for sports analysis. Random regression forest voting to localize joints of the athlete's anatomy is followed by random verification forests that evaluate and optimize the votes to improve the accuracy of clustering that determine the final position of anatomic joints. Experiential results show that the proposed algorithm enhances not only accuracy, but also efficiency of human pose estimation. We also conduct the field study to investigate feasibility of the algorithm for sports applications with developed golf swing analyzing system.
KSP 제안 키워드
3D human pose estimation, Accuracy of clustering, Golf swing, Marker-less, Performance analysis, Random forest, Random verification, Sports analysis, analyzing system, estimation algorithm, field study