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Conference Paper
멀티태스크 학습을 활용한 실세계 쓰러진 사람 탐지 기술 개발
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- Issue Date
- 2023-06
- Citation
- 대한전자공학회 학술 대회 (하계) 2023, pp.1125-1128
- Publisher
- 대한전자공학회
- Language
- Korean
- Type
- Conference Paper
- Abstract
- 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.