Visual perception enhancement fall detection algorithm based on vision transformer

Fall detection is a crucial research topic in public healthcare. With advances in intelligent surveillance and deep learning, vision-based fall detection has gained significant attention. While numerous deep learning algorithms prevail in video fall detection due to excellent feature processing capa...

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Vydané v:	Signal, image and video processing Ročník 19; číslo 1; s. 18
Hlavní autori:	Cai, Xi, Wang, Xiangcheng, Bao, Kexin, Chen, Yinuo, Jiao, Yin, Han, Guang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.01.2025 Springer Nature B.V
Predmet:	Accuracy Algorithms Computer Imaging Computer Science Deep learning Design Efficiency Fall detection Image Processing and Computer Vision Machine learning Multimedia Information Systems Neural networks Original Paper Pattern Recognition and Graphics Signal,Image and Speech Processing Time series Vision Visual perception Visual perception driven algorithms Deep learning Computer vision Vision transformer Attention mechanism Fall detection
ISSN:	1863-1703, 1863-1711
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Abstract	Fall detection is a crucial research topic in public healthcare. With advances in intelligent surveillance and deep learning, vision-based fall detection has gained significant attention. While numerous deep learning algorithms prevail in video fall detection due to excellent feature processing capabilities, they all exhibit limitations in handling long-term spatiotemporal dependencies. Recently, Vision Transformer has shown considerable potential in integrating global information and understanding long-term spatiotemporal dependencies, thus providing novel solutions. In view of this, we propose a visual perception enhancement fall detection algorithm based on Vision Transformer. We utilize Vision Transformer-Base as the baseline model for analyzing global motion information in videos. On this basis, to address the model’s difficulty in capturing subtle motion changes across video frames, we design an inter-frame motion information enhancement module. Concurrently, we propose a locality perception enhancement self-attention mechanism to overcome the model’s weak focus on local key features within the frame. Experimental results show that our method achieves notable performance on the Le2i and UR datasets, surpassing several advanced methods.
AbstractList	Fall detection is a crucial research topic in public healthcare. With advances in intelligent surveillance and deep learning, vision-based fall detection has gained significant attention. While numerous deep learning algorithms prevail in video fall detection due to excellent feature processing capabilities, they all exhibit limitations in handling long-term spatiotemporal dependencies. Recently, Vision Transformer has shown considerable potential in integrating global information and understanding long-term spatiotemporal dependencies, thus providing novel solutions. In view of this, we propose a visual perception enhancement fall detection algorithm based on Vision Transformer. We utilize Vision Transformer-Base as the baseline model for analyzing global motion information in videos. On this basis, to address the model’s difficulty in capturing subtle motion changes across video frames, we design an inter-frame motion information enhancement module. Concurrently, we propose a locality perception enhancement self-attention mechanism to overcome the model’s weak focus on local key features within the frame. Experimental results show that our method achieves notable performance on the Le2i and UR datasets, surpassing several advanced methods.
ArticleNumber	18
Author	Chen, Yinuo Han, Guang Wang, Xiangcheng Jiao, Yin Bao, Kexin Cai, Xi
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Cites_doi	10.1109/JBHI.2018.2808281 10.1016/j.cmpb.2014.09.005 10.1109/ACCESS.2020.3021795 10.3390/s23115212 10.3390/healthcare10010172 10.1109/WACV56688.2023.00397 10.1109/CVPR.2018.00745 10.1109/ACCESS.2021.3054469 10.1016/j.patrec.2018.08.031 10.1007/978-3-030-01234-2_1 10.1016/j.compbiomed.2022.105626 10.1109/EMBC44109.2020.9175844 10.1007/978-3-031-19778-9_18 10.1145/3136755.3136802 10.1007/978-3-031-27066-6_20 10.1109/ICCV.2017.74 10.1093/ageing/afac205 10.1007/s12652-021-03250-5 10.3390/electronics10080898 10.1109/ICASSP39728.2021.9414568 10.3390/diagnostics12123060 10.1145/3474085.3475272 10.1109/ICPR48806.2021.9413331 10.1016/j.jvcir.2021.103407 10.1117/1.JEI.22.4.041106 10.3390/ijerph20136284 10.1109/ICCV48922.2021.00988 10.18653/v1/D19-1224
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SubjectTerms	Accuracy Algorithms Computer Imaging Computer Science Deep learning Design Efficiency Fall detection Image Processing and Computer Vision Machine learning Multimedia Information Systems Neural networks Original Paper Pattern Recognition and Graphics Signal,Image and Speech Processing Time series Vision Visual perception Visual perception driven algorithms
Title	Visual perception enhancement fall detection algorithm based on vision transformer
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