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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2306.10159 |
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| _version_ | 1866929283381854208 |
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| author | Hasan, Md Zahid Chen, Jiajing Wang, Jiyang Rahman, Mohammed Shaiqur Joshi, Ameya Velipasalar, Senem Hegde, Chinmay Sharma, Anuj Sarkar, Soumik |
| author_facet | Hasan, Md Zahid Chen, Jiajing Wang, Jiyang Rahman, Mohammed Shaiqur Joshi, Ameya Velipasalar, Senem Hegde, Chinmay Sharma, Anuj Sarkar, Soumik |
| contents | Recognizing the activities causing distraction in real-world driving scenarios is critical for ensuring the safety and reliability of both drivers and pedestrians on the roadways. Conventional computer vision techniques are typically data-intensive and require a large volume of annotated training data to detect and classify various distracted driving behaviors, thereby limiting their efficiency and scalability. We aim to develop a generalized framework that showcases robust performance with access to limited or no annotated training data. Recently, vision-language models have offered large-scale visual-textual pretraining that can be adapted to task-specific learning like distracted driving activity recognition. Vision-language pretraining models, such as CLIP, have shown significant promise in learning natural language-guided visual representations. This paper proposes a CLIP-based driver activity recognition approach that identifies driver distraction from naturalistic driving images and videos. CLIP's vision embedding offers zero-shot transfer and task-based finetuning, which can classify distracted activities from driving video data. Our results show that this framework offers state-of-the-art performance on zero-shot transfer and video-based CLIP for predicting the driver's state on two public datasets. We propose both frame-based and video-based frameworks developed on top of the CLIP's visual representation for distracted driving detection and classification tasks and report the results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_10159 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos Hasan, Md Zahid Chen, Jiajing Wang, Jiyang Rahman, Mohammed Shaiqur Joshi, Ameya Velipasalar, Senem Hegde, Chinmay Sharma, Anuj Sarkar, Soumik Computer Vision and Pattern Recognition Recognizing the activities causing distraction in real-world driving scenarios is critical for ensuring the safety and reliability of both drivers and pedestrians on the roadways. Conventional computer vision techniques are typically data-intensive and require a large volume of annotated training data to detect and classify various distracted driving behaviors, thereby limiting their efficiency and scalability. We aim to develop a generalized framework that showcases robust performance with access to limited or no annotated training data. Recently, vision-language models have offered large-scale visual-textual pretraining that can be adapted to task-specific learning like distracted driving activity recognition. Vision-language pretraining models, such as CLIP, have shown significant promise in learning natural language-guided visual representations. This paper proposes a CLIP-based driver activity recognition approach that identifies driver distraction from naturalistic driving images and videos. CLIP's vision embedding offers zero-shot transfer and task-based finetuning, which can classify distracted activities from driving video data. Our results show that this framework offers state-of-the-art performance on zero-shot transfer and video-based CLIP for predicting the driver's state on two public datasets. We propose both frame-based and video-based frameworks developed on top of the CLIP's visual representation for distracted driving detection and classification tasks and report the results. |
| title | Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2306.10159 |