Students' Projects
Drowsy Driver Alert System
| Department | Computer Engineering |
| Year | 2022-23 |
| Budget | 28200 |
| Students | Harsh Sanghavi, Parth Thakkar, Shaikh Mo. Kaif, Dhruv Shah |
| Mentor | Prof. Vinodray Thumar, Prof. Jaimin Shroff |
| Abstract | In the current landscape, there is a growing concern regarding accidents caused by drowsy or fatigued drivers. Many companies and organizations have invested significant research and development efforts into the field of drowsy driver detection systems, resulting in various models being developed. The novelty being proposed with the drowsy driver detection system is the use of advanced computer vision and machine learning algorithms to detect signs of drowsiness in real-time. The system analyzes the driver's face and eyes to detect signs of fatigue or drowsiness, such as drooping eyelids, slow blinking. The proposed project utilizes open-source libraries such as OpenCV, dlib, and face recognition, which are easily accessible and customizable. This allows for a wider range of developers to access the technology and make modifications as needed. The use of a Raspberry Pi device and a camera makes the proposed project more affordable than many existing commercial solutions, which can be expensive and require specialized hardware. The proposed project detects drowsiness in real-time by monitoring the driver's eyes for signs of sleepiness, such as closing eyelids or drooping head. This is a key feature as it allows for timely alerts to prevent accidents caused by drowsy driving. The proposed project does not require any intrusive or uncomfortable devices to be worn by the driver, which can improve driver comfort and prevent distractions while driving. |