Accident alert system
| dc.contributor.author | Khan, Mubashir | |
| dc.contributor.author | Ansari, Mohd Aamir Mohd Sharif (17CO38) | |
| dc.contributor.author | Nathani, Aamir Haneef (17CO39) | |
| dc.contributor.author | Khan, Nazim Matiullah (17CO31) | |
| dc.contributor.author | Ansari, Faisal (17DCO62) | |
| dc.date.accessioned | 2021-12-13T05:10:16Z | |
| dc.date.available | 2021-12-13T05:10:16Z | |
| dc.date.issued | 2021-05 | |
| dc.description.abstract | Nowadays, Driver drowsiness is one of the major cause for most of the accidents in the world. Detecting the driver eye tiredness is the easiest way for measuring the drowsiness of driver. The existing systems in the literature, are providing slightly less accurate results due to low clarity in images and videos, which may result due to variations in the camera positions. In order to solve this problem, a driver drowsiness detection system is proposed in this paper, which makes use of eye blink counts for detecting the drowsiness. Specifically, the proposed framework, continuously analyzes the eye movement of the driver and alerts the driver by activating the vibrator when he/she is drowsy. When the eyes are detected closed for too long time, a vibrator signal is generated to warn the driver. The experimental results of the proposed system, which is implemented on Open CV and Raspberry Pi environment with a single camera view, illustrate the good performance of the system in terms of accurate drowsiness detection results and thereby reduces the road accidents. Keywords: Drowsiness, Fatigue Detection, Raspberry Pi, Image Processing, Eye Detection, EAR | en_US |
| dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/3778 | |
| dc.language.iso | en | en_US |
| dc.publisher | AIKTC | en_US |
| dc.subject | Project Report - CO | en_US |
| dc.title | Accident alert system | en_US |
| dc.type | Project Report | en_US |