Using SlowFast Networks for Near-Miss Incident Analysis in Dashcam Videos
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916508930670592 |
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| author | Zhang, Yucheng Emura, Koichi Watanabe, Eiji |
| author_facet | Zhang, Yucheng Emura, Koichi Watanabe, Eiji |
| contents | This paper classifies near-miss traffic videos using the SlowFast deep neural network that mimics the characteristics of the slow and fast visual information processed by two different streams from the M (Magnocellular) and P (Parvocellular) cells of the human brain. The approach significantly improves the accuracy of the traffic near-miss video analysis and presents insights into human visual perception in traffic scenarios. Moreover, it contributes to traffic safety enhancements and provides novel perspectives on the potential cognitive errors in traffic accidents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03903 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using SlowFast Networks for Near-Miss Incident Analysis in Dashcam Videos Zhang, Yucheng Emura, Koichi Watanabe, Eiji Artificial Intelligence This paper classifies near-miss traffic videos using the SlowFast deep neural network that mimics the characteristics of the slow and fast visual information processed by two different streams from the M (Magnocellular) and P (Parvocellular) cells of the human brain. The approach significantly improves the accuracy of the traffic near-miss video analysis and presents insights into human visual perception in traffic scenarios. Moreover, it contributes to traffic safety enhancements and provides novel perspectives on the potential cognitive errors in traffic accidents. |
| title | Using SlowFast Networks for Near-Miss Incident Analysis in Dashcam Videos |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2412.03903 |