Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos

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 da...

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Bibliographic Details
Published inarXiv.org
Main Authors Md Zahid Hasan, Chen, Jiajing, Wang, Jiyang, Rahman, Mohammed Shaiqur, Joshi, Ameya, Velipasalar, Senem, Hegde, Chinmay, Sharma, Anuj, Sarkar, Soumik
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 21.03.2024
Subjects
Activity recognition
Classification
Computer vision
Distracted driving
Driver behavior
Learning
Natural language (computers)
Pedestrians
Representations
Training
Video data
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Summary: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.
ISSN:2331-8422