Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916940143919104 |
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| author | Akter, Sanjeda Shihab, Ibne Farabi Sharma, Anuj |
| author_facet | Akter, Sanjeda Shihab, Ibne Farabi Sharma, Anuj |
| contents | Crash detection from video feeds is a critical problem in intelligent transportation systems. Recent developments in large language models (LLMs) and vision-language models (VLMs) have transformed how we process, reason about, and summarize multimodal information. This paper surveys recent methods leveraging LLMs for crash detection from video data. We present a structured taxonomy of fusion strategies, summarize key datasets, analyze model architectures, compare performance benchmarks, and discuss ongoing challenges and opportunities. Our review provides a foundation for future research in this fast-growing intersection of video understanding and foundation models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02074 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Akter, Sanjeda Shihab, Ibne Farabi Sharma, Anuj Computer Vision and Pattern Recognition Artificial Intelligence Crash detection from video feeds is a critical problem in intelligent transportation systems. Recent developments in large language models (LLMs) and vision-language models (VLMs) have transformed how we process, reason about, and summarize multimodal information. This paper surveys recent methods leveraging LLMs for crash detection from video data. We present a structured taxonomy of fusion strategies, summarize key datasets, analyze model architectures, compare performance benchmarks, and discuss ongoing challenges and opportunities. Our review provides a foundation for future research in this fast-growing intersection of video understanding and foundation models. |
| title | Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2507.02074 |