열화상 카메라를 이용한 3D 컨볼루션 신경망 기반 낙상 인식 3D Convolutional Neural Networks based Fall Detection with Thermal Camera

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This paper presents a vision-based fall detection system to automatically monitor and detect people’s fall accidents, particularly those of elderly people or patients. For video analysis, the system should be able to extract both spatial and temporal features so that the model captures appearance and motion information simultaneously. Our approach is based on 3-dimensional convolutional neural networks, which can learn spatiotemporal features. In addition, we adopts a thermal camera in order to handle several issues regarding usability, day and night surveillance and privacy concerns. We design a pan-tilt camera with two actuators to extend the range of view. Performance is evaluated on our thermal dataset: TCL Fall Detection Dataset. The proposed model achieves 90.2% average clip accuracy which is better than other approaches.
Publisher
한국로봇학회
Issue Date
2018
Language
Korean
Keywords

3D Convolutional neural networks; Fall detection; Thermal images

Citation

로봇학회 논문지, v.13, no.1, pp.45 - 54

ISSN
1975-6291
DOI
10.7746/jkros.2018.13.1.045
URI
http://hdl.handle.net/10203/246981
Appears in Collection
ME-Journal Papers(저널논문)
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