Title: A computer vision monitoring for human fall using visible light camera and thermal imager

Authors: Baolong Yuan; Yanhui Wang; Xin Wang

Addresses: School of Electrical and Control Engineering, Shenyang Jianzhu University, 110168, China ' Baoding Vocational and Technical College, Baoding, Hebei, 071051, China ' School of Electrical and Control Engineering, Shenyang Jianzhu University, 110168, China

Abstract: In order to solve the problems of image blur, uneven illumination and object occlusion in visual monitoring, a human fall detection algorithm based on visible light camera and thermal imager is proposed in this paper. Firstly, the visible light and thermal images are denoised to reduce the interference of noise. Secondly, the skeleton and joint coordinates of the human body are extracted through the lightweight human posture recognition model. Finally, three human posture parameters are designed as recognition features to achieve accurate fall recognition. The method is verified on self-built datasets and public datasets. The experimental results show that the accuracy of the method is 0.93 and 0.94 respectively. Compared with the most advanced algorithms, the proposed method has higher accuracy and better real-time performance.

Keywords: fall detection; deep neural network; multi-source image fusion; computer vision.

DOI: 10.1504/IJMIC.2023.132608

International Journal of Modelling, Identification and Control, 2023 Vol.43 No.2, pp.145 - 153

Received: 04 Jul 2022
Received in revised form: 20 Sep 2022
Accepted: 30 Sep 2022
Published online: 30 Jul 2023 *

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