Visual perception enhancement fall detection algorithm based on vision transformer
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| Publicado en: | Signal, Image and Video Processing vol. 19, no. 1 (Jan 2025), p. 18 |
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| Autor principal: | |
| Otros Autores: | , , , , |
| Publicado: |
Springer Nature B.V.
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 1863-1703 | ||
| 022 | |a 1863-1711 | ||
| 024 | 7 | |a 10.1007/s11760-024-03652-w |2 doi | |
| 035 | |a 3256966131 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
| 100 | 1 | |a Cai, Xi |u Northeastern University at Qinhuangdao, Hebei Key Laboratory of Marine Perception Network and Data Processing, School of Computer and Communication Engineering, Qinhuangdao, China (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359) | |
| 245 | 1 | |a Visual perception enhancement fall detection algorithm based on vision transformer | |
| 260 | |b Springer Nature B.V. |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Fall detection | ||
| 653 | |a Accuracy | ||
| 653 | |a Vision | ||
| 653 | |a Deep learning | ||
| 653 | |a Visual perception | ||
| 653 | |a Video recordings | ||
| 653 | |a Neural networks | ||
| 653 | |a Design | ||
| 653 | |a Algorithms | ||
| 653 | |a Machine learning | ||
| 653 | |a Time series | ||
| 653 | |a Visual perception driven algorithms | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Wang, Xiangcheng |u Northeastern University at Qinhuangdao, Hebei Key Laboratory of Marine Perception Network and Data Processing, School of Computer and Communication Engineering, Qinhuangdao, China (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359) | |
| 700 | 1 | |a Bao, Kexin |u Northeastern University at Qinhuangdao, Hebei Key Laboratory of Marine Perception Network and Data Processing, School of Computer and Communication Engineering, Qinhuangdao, China (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359) | |
| 700 | 1 | |a Chen, Yinuo |u Northeastern University at Qinhuangdao, Hebei Key Laboratory of Marine Perception Network and Data Processing, School of Computer and Communication Engineering, Qinhuangdao, China (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359) | |
| 700 | 1 | |a Jiao, Yin |u Northeastern University at Qinhuangdao, Hebei Key Laboratory of Marine Perception Network and Data Processing, School of Computer and Communication Engineering, Qinhuangdao, China (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359) | |
| 700 | 1 | |a Han, Guang |u Northeastern University at Qinhuangdao, Hebei Key Laboratory of Marine Perception Network and Data Processing, School of Computer and Communication Engineering, Qinhuangdao, China (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359) | |
| 773 | 0 | |t Signal, Image and Video Processing |g vol. 19, no. 1 (Jan 2025), p. 18 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3256966131/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3256966131/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3256966131/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |