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학술대회 The Dataset and Baseline Models to Detect Human Postural States Robustly against Irregular Postures
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저자
배강민, 윤기민, 조정찬, 배유석
발행일
202111
출처
International Conference on Advanced Video and Signal-based Surveillance (AVSS) 2021, pp.1-8
DOI
https://dx.doi.org/10.1109/AVSS52988.2021.9663782
협약과제
21HS4600, (딥뷰-1세부) 실시간 대규모 영상 데이터 이해·예측을 위한 고성능 비주얼 디스커버리 플랫폼 개발, 배유석
초록
In many visual applications, we often encounter people with irregular postures, such as lying down. Many approaches adopted two-step methods to handle a person with irregular postures: 1) person detection and 2) posture prediction based on the detected person. However, it is challenging to detect irregular postures because the existing detectors were trained with datasets consisting of most upright postures. Therefore, we propose a new Irregular Human Posture (IHP) dataset to handle various postures captured from real-world surveillance cameras. The IHP dataset provides sufficient annotations to understand the posture of person, including segmentation, keypoints, and postural states. This paper also provides two baseline net-works for postural state estimation of the people trained on the IHP dataset. Moreover, we show that our baseline networks effectively detect the people with irregular postures that may be in an urgent situation in a surveillance environment.
KSP 제안 키워드
Human Posture, Person detection, Posture prediction, Real-world, Two-Step, state estimation(SE), surveillance camera