Research on Real-Time Human Fall Detection Method Based on YOLOv5-Lite-Reference-Cited by-同舟云学术

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Research on Real-Time Human Fall Detection Method Based on YOLOv5-Lite

Published:2023-07-21 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 6th International Conference on Electronic Information and Communication Technology (ICEICT)
language:
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Author:

Cheng Baolei¹,Su Yaying¹,Cai Yijun¹

Affiliation:

1. Smart Sensing Integrated Circuit Engineering Research Center of Universities in Fujian Province, Xiamen University of Technology,Xiamen,China,361024

Funder

National Natural Science Foundation of China

Natural Science Foundation of Fujian Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10244769/10244774/10245195.pdf?arnumber=10245195

Reference11 articles.

1. Improved YOLOv5 Method for Fall Detection

2. DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

3. Are wearable devices effective for preventing and detecting falls: an umbrella review (a review of systematic reviews)

4. Application of structured nursing program based on geriatric comprehensive assessment in prevention of falls among elderly people outside hospital [J];sun;Nursing Research,2019

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep learning for computer vision based activity recognition and fall detection of the elderly: a systematic review;Applied Intelligence;2024-07-08

2. The Accelerated Inference of a Novel Optimized YOLOv5-LITE on Low-Power Devices for Railway Track Damage Detection;IEEE Access;2023

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