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학술지 Fast Random-Forest-Based Human Pose Estimation Using a Multi-scale and Cascade Approach
Cited 17 time in scopus Download 1 time Share share facebook twitter linkedin kakaostory
저자
장주용, 남승우
발행일
201312
출처
ETRI Journal, v.35 no.6, pp.949-959
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.13.2013.0063
협약과제
12MT1300, 차세대 체감형 콘텐츠를 위한 사용자 동작기반 플랫폼 및 입체 상호작용기술개발, 남승우
초록
Since the recent launch of Microsoft Xbox Kinect, research on 3D human pose estimation has attracted a lot of attention in the computer vision community. Kinect shows impressive estimation accuracy and real-time performance on massive graphics processing unit hardware. In this paper, we focus on further reducing the computation complexity of the existing state-of-the-art method to make the real-time 3D human pose estimation functionality applicable to devices with lower computing power. As a result, we propose two simple approaches to speed up the random-forest-based human pose estimation method. In the original algorithm, the random forest classifier is applied to all pixels of the segmented human depth image. We first use a multi-scale approach to reduce the number of such calculations. Second, the complexity of the random forest classification itself is decreased by the proposed cascade approach. Experiment results for real data show that our method is effective and works in real time (30 fps) without any parallelization efforts. © 2013 ETRI.
키워드
Depth data, Human pose estimation, Interactive digital contents, Random forest
KSP 제안 키워드
3D human pose estimation, Computer Vision(CV), Computing power, Depth Data, Depth image, Digital content, Estimation accuracy, Estimation method, Experiment results, Graphic Processing Unit(GPU), Multi-scale approach