Sanket Patil
Sujata Bhande
Ankita Hannure
Akramuddin Ahmed
- Student, Department of Computer Science, NBN Sinhgad School of Engineering, Ambegaon, Pune,, Maharashtra, India.
- Student, Department of Computer Science, NBN Sinhgad School of Engineering, Ambegaon, Pune,, Maharashtra, India
- Student, Department of Computer Science, NBN Sinhgad School of Engineering, Ambegaon, Pune,, Maharashtra, India
- Assistant Professor, Department of Computer Science, NBN Sinhgad School of Engineering, Ambegaon, Pune, Maharashtra, India
Abstract
High level Driver-Help Frameworks (ADASs) are utilized for expanding security in the auto space, yet momentum ADASs quite work without considering drivers’ states, e.g., whether she/he is genuinely able to drive. Feelings are a significant way of behaving of people and may emerge in driving circumstances. Uncontrolled feelings can prompt unsafe impacts. To control and decrease the adverse consequence of conduct. In this paper we will distinguish the driver’s conduct. We are going to chip away at five classifications, for example, driver messaging, driver turning, safe driving, talking and other movement. By utilizing a convolutional brain network, we are goin to characterize driver behavior. The convolutional brain network extricates the highlights as well as arranges the classification or conduct. In this paper we are trained model on 100 epochs, and we achieve 92.23% accuracy.
Keywords: Convolutional Neural Network, Deep Learning, Image Processing, Object Classification
[This article belongs to International Journal of Electronics Automation(ijea)]
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References
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| Volume | 01 |
| Issue | 01 |
| Received | May 16, 2023 |
| Accepted | July 18, 2023 |
| Published | November 29, 2023 |