In-the-wild Drowsiness Detection from Facial Expressions
| Autor: | Survi Kyal, Ajjen Joshi, Sandipan Banerjee, Taniya Mishra |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
050210 logistics & transportation Facial expression Hardware_MEMORYSTRUCTURES Artificial neural network Computer science business.industry Property (programming) Computer Vision and Pattern Recognition (cs.CV) 05 social sciences Computer Science - Computer Vision and Pattern Recognition Baseline model 020207 software engineering Pattern recognition 02 engineering and technology Expression (mathematics) Face (geometry) 0502 economics and business 0202 electrical engineering electronic engineering information engineering Artificial intelligence Data collection protocol business Hidden Markov model |
| DOI: | 10.48550/arxiv.2010.11162 |
| Popis: | Driving in a state of drowsiness is a major cause of road accidents, resulting in tremendous damage to life and property. Developing robust, automatic, real-time systems that can infer drowsiness states of drivers has the potential of making life-saving impact. However, developing drowsiness detection systems that work well in real-world scenarios is challenging because of the difficulties associated with collecting high-volume realistic drowsy data and modeling the complex temporal dynamics of evolving drowsy states. In this paper, we propose a data collection protocol that involves outfitting vehicles of overnight shift workers with camera kits that record their faces while driving. We develop a drowsiness annotation guideline to enable humans to label the collected videos into 4 levels of drowsiness: `alert', `slightly drowsy', `moderately drowsy' and `extremely drowsy'. We experiment with different convolutional and temporal neural network architectures to predict drowsiness states from pose, expression and emotion-based representation of the input video of the driver's face. Our best performing model achieves a macro ROC-AUC of 0.78, compared to 0.72 for a baseline model. Comment: Paper from HSIM Workshop at IEEE Intelligent Vehicles Symposium 2020 (IV2020) |
| Databáze: | OpenAIRE |
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