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Fall Behavior Recognition Based on Deep Learning and Image Processing

Fall Behavior Recognition Based on Deep Learning and Image Processing

He Xu, Leixian Shen, Qingyun Zhang, Guoxu Cao
Copyright: © 2020 |Pages: 16
ISBN13: 9781799804147|ISBN10: 1799804143|EISBN13: 9781799804154
DOI: 10.4018/978-1-7998-0414-7.ch078
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MLA

Xu, He, et al. "Fall Behavior Recognition Based on Deep Learning and Image Processing." Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 1394-1409. https://doi.org/10.4018/978-1-7998-0414-7.ch078

APA

Xu, H., Shen, L., Zhang, Q., & Cao, G. (2020). Fall Behavior Recognition Based on Deep Learning and Image Processing. In I. Management Association (Ed.), Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications (pp. 1394-1409). IGI Global. https://doi.org/10.4018/978-1-7998-0414-7.ch078

Chicago

Xu, He, et al. "Fall Behavior Recognition Based on Deep Learning and Image Processing." In Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1394-1409. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-0414-7.ch078

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Abstract

Accidental fall detection for the elderly who live alone can minimize the risk of death and injuries. In this article, we present a new fall detection method based on "deep learning and image, where a human body recognition model-DeeperCut is used. First, a camera is used to get the detection source data, and then the video is split into images which can be input into DeeperCut model. The human key point data in the output map and the label of the pictures are used as training data to input into the fall detection neural network. The output model then judges the fall of the subsequent pictures. In addition, the fall detection system is designed and implemented with using Raspberry Pi hardware in a local network environment. The presented method obtains a 100% fall detection rate in the experimental environment. The false positive rate on the test set is around 1.95% which is very low and can be ignored because this will be checked by using SMS, WeChat and other SNS tools to confirm falls. Experimental results show that the proposed fall behavior recognition is effective and feasible to be deployed in home environment.

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