Spiking-DD: Neuromorphic Event Camera based Driver Distraction Detection with Spiking Neural Network
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866916415486820352 |
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| author | Shariff, Waseem Kielty, Paul Lemley, Joseph Corcoran, Peter |
| author_facet | Shariff, Waseem Kielty, Paul Lemley, Joseph Corcoran, Peter |
| contents | Event camera-based driver monitoring is emerging as a pivotal area of research, driven by its significant advantages such as rapid response, low latency, power efficiency, enhanced privacy, and prevention of undersampling. Effective detection of driver distraction is crucial in driver monitoring systems to enhance road safety and reduce accident rates. The integration of an optimized sensor such as Event Camera with an optimized network is essential for maximizing these benefits. This paper introduces the innovative concept of sensing without seeing to detect driver distraction, leveraging computationally efficient spiking neural networks (SNN). To the best of our knowledge, this study is the first to utilize event camera data with spiking neural networks for driver distraction. The proposed Spiking-DD network not only achieve state of the art performance but also exhibit fewer parameters and provides greater accuracy than current event-based methodologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20633 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spiking-DD: Neuromorphic Event Camera based Driver Distraction Detection with Spiking Neural Network Shariff, Waseem Kielty, Paul Lemley, Joseph Corcoran, Peter Computer Vision and Pattern Recognition 68 Event camera-based driver monitoring is emerging as a pivotal area of research, driven by its significant advantages such as rapid response, low latency, power efficiency, enhanced privacy, and prevention of undersampling. Effective detection of driver distraction is crucial in driver monitoring systems to enhance road safety and reduce accident rates. The integration of an optimized sensor such as Event Camera with an optimized network is essential for maximizing these benefits. This paper introduces the innovative concept of sensing without seeing to detect driver distraction, leveraging computationally efficient spiking neural networks (SNN). To the best of our knowledge, this study is the first to utilize event camera data with spiking neural networks for driver distraction. The proposed Spiking-DD network not only achieve state of the art performance but also exhibit fewer parameters and provides greater accuracy than current event-based methodologies. |
| title | Spiking-DD: Neuromorphic Event Camera based Driver Distraction Detection with Spiking Neural Network |
| topic | Computer Vision and Pattern Recognition 68 |
| url | https://arxiv.org/abs/2407.20633 |