Driver fatigue is one the leading causes of car accidents in the world. Detecting drowsiness and alerting the driver is the easiest way to prevent mishaps. The purpose of this paper is to develop a fatigue detection and alert system. This system works by analyzing the eye closure duration and yawn frequency of the driver and alerting the driver by activating LEDs, buzzers and sending warning message to his emergency contacts. The alerts are divided into three stages of severity to take action accordingly. Facial features for determining alertness were obtained by using a camera capturing the face of the driver. The system can monitor the driver's eyes to detect early stages of sleep as well as short periods of sleep lasting 3 to 4 seconds. The application is implemented on a Raspberry Pi minicomputer with a NoIR camera, making the system economical and portable. The system not only provides an effective way to detect fatigue but also provides many forms of alerts to control the situation and compel the driver to take a break.
Character segmentation is a necessary preprocessing step for character recognition in many license plate recognition systems. It is important because incorrectly segmented characters are less likely to be recognized correctly. In this article segmentation scheme of Indian LP has been proposed with some unconventional and easy-to implement technique -The connected component analysis. In this paper the first part is taken into consideration some preprocessing operations and the second part is for connected component analysis by means of labelling algorithm, aspect ratio analysis and pixel count analysis. By using the corresponding co-ordinates, characters are extracted from the license plate. Results for this method are promising even in tough conditions.
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