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학술지 Robust Human Pose Estimation for Rotation via Self-Supervised Learning
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저자
윤기민, 박종열, 조정찬
발행일
202002
출처
IEEE Access, v.8, pp.32502-32517
ISSN
2169-3536
출판사
IEEE
DOI
https://dx.doi.org/10.1109/ACCESS.2020.2973390
협약과제
19HS3400, (딥뷰-1세부) 실시간 대규모 영상 데이터 이해·예측을 위한 고성능 비주얼 디스커버리 플랫폼 개발, 박종열
초록
The detection of abnormal postures, such as that of a reclining person, is a crucial part of visual surveillance. Further, even regular poses can appear rotated because of incongruity between the image and the angle of a pre-installed camera. However, most existing human pose estimation methods focus on small rotational changes, i.e., those less than 50 degrees, and they seldom consider robust human pose estimation for more drastic rotational changes. To the best of our knowledge, there have been no reports on the robustness of human pose estimation for rotational changes through large angles. In this study, we propose a robust human pose estimation method by creating a path for learning new rotational changes based on a self-supervised method and by combining the results with those obtained from a path based on a supervised method. Furthermore, a combination module composed of a convolutional layer is trained complementarily by both paths of the network to produce robust results for various rotational changes. We demonstrate the robustness of the proposed method with extensive experiments on images generated by rotating the elements of standard benchmark datasets. We fully analyze the rotational characteristics of the state-of-the-art human pose estimators and the proposed method. On the COCO Keypoint Detection dataset, the proposed method attains more than 15% improvement in the mean of average precision compared to the state-of-the-art method, and the standard deviation of the performance is improved by more than 4.7 times.
키워드
Deep learning, human pose estimation, rotation, self-supervised learning
KSP 제안 키워드
Average Precision, Benchmark datasets, Estimation method, Human pose estimation, Keypoint Detection, Standard deviation(STD), deep learning(DL), self-supervised learning, state-of-The-Art, supervised method, visual surveillance
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