상세정보
학술대회
Localizing Human Keypoints beyond the Bounding Box
- 저자
- 박순찬, 박진아
- 발행일
- 202110
- 출처
- International Conference on Computer Vision Workshops (ICCVW) 2021, pp.1602-1611
- 협약과제
- 21IH1400, 5G를 활용하는 차세대 1인 콘텐츠 기반의 문화상품 커뮤니케이션 마켓 플랫폼 개발, 박지영
- 초록
- Since human pose is one of the most effective and popular sources for understanding human in various applications, there have been numerous researches on detecting keypoints of human body from the image source. However, when a human body is shown partially in the source image, estimation range is also restricted causing performance degradation in locating keypoints of human body. In this paper, we propose 'Position Puzzle' network and augmentation to leverage the performance of detecting keypoints including those outside the bounding box. Specifically, Position Puzzle Network expands the spatial range of keypoint localization by refining the position and the scale of the target's bounding box, and Position Puzzle Augmentation improves the performance of keypoint detector using the partial image in training. We prepare data by cropping COCO dataset and utilize them in training and evaluation. Under the prepared dataset, the proposed method enhances the performance of baseline network up to 37.6% and 30.6% in mAP and mAR, respectively, and effectively localizes keypoints positioned not only inside but also outside the bounding box. We also verify that the proposed method can localize keypoints beyond the bounding box in the original COCO dataset.
- KSP 제안 키워드
- Baseline network, Bounding Box, Human body, Human pose, Image source, Keypoint detector, Training and evaluation, performance degradation