ETRI Knowledge Sharing Platform : 3D Human Pose Machine with a ToF Sensor using Pre-trained Convolutional Neural Networks

Titles

논문 검색
Type		SCI
Year	~	Keyword

List

Conference Paper 3D Human Pose Machine with a ToF Sensor using Pre-trained Convolutional Neural Networks

Cited 3 time in scopus

Citation: International Conference on Information and Communication Technology Convergence (ICTC) 2019, pp.1018-1020

Abstract: This paper proposes a new system for estimating 3D human pose with a ToF sensor. In the proposed system, a single low-cost ToF sensor captures depth data of human pose. Then, a new clean imaging converter for the ToF sensor transforms the depth data corrupted with sensor errors and zero values into a clean 3D image data. Finally, a deep learning predictor based on the convolutional neural networks pre-trained with millions of 2D image data of human pose, not 3D ones, estimates the 3D human pose from the clean 3D image data. Experimental results show that the proposed system shows the good performance comparable to the commercial system in terms of 3D human pose estimation accuracy.

KSP Keywords: 2D image, 3D Image, 3D human pose estimation, Convolution neural network(CNN), Depth Data, Estimation accuracy, Image data, Low-cost, deep learning(DL), sensor errors

218 Gajeong-ro, Yuseong-gu, Daejeon, 34129, KOREA, Contact: sh.kim@etri.re.kr

Please refrain from automatic collection of e-mail addresses posted on this homepage.