Traffic accidents due to human error causes many death and injuries around in the world.




Traffic accidents due to human error causes many death and injuries around in the world. Driver drowsiness is leading causes of motor vehicle crashes. The sensation of drowsiness diminishes the level of vigilance of the driver and results in perilous situations. While eminent automobile manufacturers like Volvo, Mercedes-Benz and Bosch have ventured into the development of drowsiness detection technologies, use of these safety systems is not widespread among drivers due to their availability in luxury cars only.


The Goal of this thesis project proposes a non-intrusive Real-Time Driver Drowsiness Detection System using Deep Learning and Raspberry Pi to utilize the driving video dataset developed by the University of Texas, Arlington for training and testing the deep learning model. The developed model will then be deployed on Raspberry Pi (with Pi Camera module) to make predictions and alert the driver in real time. Road crashes and related forms of accident are common cause of injury and death.  The device is currently essential in many fields for sleepiness related accident prevention. Real-time driver drowsiness system alerts users when they are fall in a sleep. Real Time Driver Drowsiness Detection system is the complete system is implemented on Raspberry Pi which uses a webcam to monitor user’s facial Expression and average Face duration to detect drowsiness. The project purpose to a car safety which help to prevent accidents cause by the driver get to drowsy. The motive of this project is to detect multi-stage drowsiness target not only extreme and easily visible case but also subtitle cases when micro-expressions are the discriminative factors. Detection of these subtle cases can be important for detecting drowsiness at an early stage, so as to activate drowsiness prevention mechanisms. The project aims to build a computationally inexpensive deep learning model that can be deployed on Raspberry Pi. The model developed will receive video input from the Pi Camera module placed on the vehicle dashboard. It will use this video stream to gauge the alertness of the driver and notify the driver when early stages of drowsiness are detected.


Keyword:-Machine learning, Driver observance System; temporary state Detection; Deep Learning; Raspberry Pi., Android, Neural network.









I am extremely fortunate to be involved in an exciting and challenging research project “Study of Real Time Driver Drowsiness Detection”.it has enriched my life, giving me an opportunity to work in a new environment of Research area. This project increased my thinking and understanding capability and after the completion of this project ,I experience the feeling of achievement and satisfaction.


I would like to express my greatest gratitude and respect to my supervisor Assistant professor Er. Pyuish rai and co-supervisor Er.Nidhi Prasad,for his excellent guidance , valuable suggestions and endless support .They have not only be able to wok under guidance of such dynamic personalities.


I express my sincere thanks to Er.pyuish rai ,Er.Awdhesh Dixit and Er. Shobhit srivastava Assistant professor, M.Tech, Department of computer science &Engineering, IET Ayodhya for their full-time support and motivation of research and development in my M.Tech program I am also grateful to Prof. Manoj Dixit (Hummable Vice-Chancellor), Prof. Rama Patti Mishra (Director) Err. Hashish kumar pandey (HOD), All faculty members and staff of IET, Dr. Ram manohar Lohia Avadh University, Ayodhya for providing all the facilities and support .


It is a pleasure to Acknowldge the support and help extended by all my colleagues. Last but not the least; I want to convey my Heartiest gratitude to my parents for their immeasurable love, support and encouragement.




Chanchal singh

181104 (computer science Engineering)

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