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학술대회 3D Human Pose Machine with a ToF Sensor using Pre-trained Convolutional Neural Networks
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저자
김종성, 권승준
발행일
201910
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2019, pp.1018-1020
DOI
https://dx.doi.org/10.1109/ICTC46691.2019.8939872
협약과제
19NE1600, 생활안전 예방서비스 기술개발 연구단, 김형준
초록
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.
키워드
convolutional neural networks, deep learning, depth data imaging, Human pose estimation, ToF sensor
KSP 제안 키워드
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