http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
멀티태스크 학습을 활용한 실세계 쓰러진 사람 탐지 기술 개발
부원국(Wonkuk Boo),배강민(Kangmin Bae),윤기민(Kimin Yun),배유석(Yuseok Bae) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
Recent advancements in deep learning have stimulated the development of various datasets pertinent to human understanding. However, only a fraction of these datasets address a range of social issues, such as fallen person detection, while the majority focus on providing human keypoints and action labels. Therefore, this paper proposes a multi-task learning approach that jointly trains annotations, including keypoints and state recognitions, to enhance fallen person detection. We offer a statistical overview of human state datasets and propose methods to reconcile discrepancies in human state labels derived from multiple domains. Additionally, we present both qualitative and quantitative results of fallen person detection using benchmark datasets.
Visual SLAM의 건설현장 실내 측위 활용성 분석
김태진 ( Kim Taejin ),박지원 ( Park Jiwon ),이병민 ( Lee Byoungmin ),배강민 ( Bae Kangmin ),윤세빈 ( Yoon Sebeen ),김태훈 ( Kim Taehoon ) 한국건축시공학회 2022 한국건축시공학회 학술발표대회 논문집 Vol.22 No.2
The positioning technology that measures the position of a person or object is a key technology to deal with the location of the real coordinate system or converge the real and virtual worlds, such as digital twins, augmented reality, virtual reality, and autonomous driving. In estimating the location of a person or object at an indoor construction site, there are restrictions that it is impossible to receive location information from the outside, the communication infrastructure is insufficient, and it is difficult to install additional devices. Therefore, this study tested the direct sparse odometry algorithm, one of the visual Simultaneous Localization and Mapping (vSLAM) that estimate the current location and surrounding map using only image information, at an indoor construction site and analyzed its applicability as an indoor positioning technology. As a result, it was found that it is possible to properly estimate the surrounding map and the current location even in the indoor construction site, which has relatively few feature points. The results of this study can be used as reference data for researchers related to indoor positioning technology for construction sites in the future.