Drowsy Driver Warning for Accident-Avoidance System Using Image Processing

Bhagwat, Kiran; Pawar, Shraddha; Sudrik, Pratibha; Waghchaure, Vishwanath; Jadhav, Sweta · 2022 · Crossref

DOI: 10.22214/ijraset.2022.47544

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Summary

This paper addresses the critical safety issue of driver fatigue, a primary cause of vehicular accidents, by proposing a non-intrusive, real-time drowsy driver warning system. The authors aim to bridge the gap between expensive, high-end automotive systems and less robust, affordable solutions by developing a prototype that balances reliability, accuracy, and speed. The system is designed to operate automatically upon ignition without requiring driver initiation or feedback, ensuring it remains effective regardless of facial texture, color, or varying lighting conditions such as shadows and reflections. The methodology relies on image processing techniques to monitor the driver’s facial features, specifically focusing on eye and mouth states. The system employs a camera positioned inside the vehicle to capture facial images, which undergo preprocessing including binarization and noise removal via median filters and expansion/contraction operations. The detection process involves identifying the maximum face width to locate lateral boundaries, followed by detecting the vertical positions of the eyes using labeling operations based on pixel area, aspect ratio, and relative coordinates. To ensure high-speed processing, the system utilizes an eye-tracking function that updates the search area for eyes in subsequent frames based on previous coordinates, rather than scanning the entire image each time. Drowsiness is determined by judging whether the eyes are open or closed using a template matching method involving concentric circles to analyze pixel brightness patterns. Additionally, the system can detect yawning and closed-mouth states, allowing it to function even if one eye is obscured by spectacles, provided both eyes and the mouth are not simultaneously covered. The study outlines specific operational scopes and limitations, noting that accurate image processing is currently restricted to daylight conditions and requires the driver to face the camera. The system is software-based and may yield different results if the driver is ill. The expected outcome demonstrates the system’s ability to segment the face from the background and monitor eye and mouth states to detect fatigue. If the eyes are open and the mouth is closed, no alarm is generated; conversely, signs of drowsiness trigger a warning. The authors highlight that this early detection mechanism can warn drivers before they fall asleep, potentially preventing loss of vehicle control. The significance of this work lies in its potential to reduce road accidents, particularly in the commercial vehicle sector where fatigue is a major risk. The authors suggest that implementing such a system could lead to a 15% reduction in fuel consumption, improved traffic flow, and enhanced safety and comfort for drivers. Future work aims to strengthen the system by integrating multiple behavioral indicators, such as iris and pupil detection, alongside eye behavior to reduce false identifications. The authors also propose expanding the application of this technology to security systems, toll collection counters, and check posts.

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Topics

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• drowsiness detection algorithms
• gaze based attention detection
• dms validation

Information type

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• Methodological Resource: tool software