Driver Alertness Identification And Alarming System Using Haar Cascade Algorithm

R. Vadivel; S. A. Kamran Kashif; Syed Sajaad Ali; Aashu Ali; Masood Baig · 2022 · Crossref

DOI: 10.32628/ijsrset229223

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Summary

This paper addresses the critical safety issue of driver fatigue and intoxication, which are major contributors to global traffic accidents. The authors note that driver fatigue accounts for approximately 30% of collisions, while drowsy driving contributes to roughly 21% of road accidents. The primary motivation is to develop a non-intrusive, real-time system that can detect a driver’s state of alertness and prevent accidents caused by drowsiness or alcohol consumption. Existing methods often rely on physiological sensors (e.g., ECG, pulse rate) that require physical contact, which can be uncomfortable, distracting, and prone to error due to sweat during long drives. Consequently, the authors propose a computer vision-based approach that monitors eye closure without physical intrusion, combined with an alcohol detection mechanism to prevent vehicle ignition if intoxication is detected. The proposed system utilizes the Haar Cascade algorithm, a computer vision technique implemented via OpenCV libraries, to detect and track human faces and eyes in real-time. The core metric for determining drowsiness is PERCLOS (Percentage of Eyelid Closure over Pupil), which calculates the ratio of time the eyes are closed to the total observation period. If the eyelid closure exceeds a specific threshold, the system identifies the driver as drowsy. Additionally, the system integrates an alcohol sensor that measures blood alcohol content before the vehicle starts. If the alcohol level exceeds a predefined threshold, the ignition is disabled. The hardware implementation was tested using a Raspberry Pi for video processing and an alcohol sensor, with the authors also suggesting that a smartphone application could serve as a more efficient alternative to the Raspberry Pi for video processing tasks. The study reports that the prototype successfully captured video in real-time and detected alcohol levels accurately. The system was able to identify drowsy states based on eye closure metrics and trigger warning signals. The authors conclude that the system effectively combines non-intrusive visual monitoring with pre-ignition alcohol checks to enhance driver safety. However, they acknowledge limitations, noting that a warning signal alone may not prevent accidents if the driver’s response time is too slow. To address this, the authors propose future enhancements, such as integrating a motor-driven system that automatically slows the vehicle upon detecting drowsiness, thereby providing a more robust safety intervention than simple alarms. The paper positions this approach as a viable, non-intrusive solution for intelligent vehicle systems, contrasting it with less practical physiological monitoring methods.

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Topics

Ranked by relevance to this paper. Hover a topic for its definition.

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

Information type

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• Empirical Findings: physiological data
• Methodological Resource: tool software