A Vision-Based System for Monitoring the Loss of Attention in Automotive Drivers

Dasgupta, Anirban; George, Anjith; Happy, S L; Routray, Aurobinda · 2013 · OpenAlex-citations

DOI: 10.1109/tits.2013.2271052

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Summary

This paper addresses the critical safety issue of driver fatigue, a major cause of road accidents, by proposing a robust, real-time vision-based system for monitoring the loss of attention in automotive drivers. The authors aim to overcome limitations in existing systems, such as sensitivity to illumination changes, head rotations, and low processing speeds, by developing an embedded platform capable of operating effectively during both day and night driving conditions. The system utilizes the PERcentage of eye CLOSure (PERCLOS) as the primary metric for alertness, defined as the proportion of time eyelids are at least 80% closed over a specific interval. The methodology involves a multi-stage algorithm implemented on a Single Board Computer (SBC) with an Intel Atom processor. Face detection is performed using Haar-like features, with search space optimization achieved through Kalman Filter tracking and image down-sampling to balance speed and accuracy. To handle head movements, the system applies Affine transformations for in-plane rotations and Perspective transformations for off-plane rotations. Illumination variations are compensated using Bi-Histogram Equalization (BHE), which preserves mean intensity while enhancing contrast. Eye detection employs Principal Component Analysis (PCA) for daytime conditions and Local Binary Pattern (LBP) features for nighttime. Finally, eye states are classified as open or closed using Support Vector Machines (SVM). The system was validated through laboratory tests and on-board trials under varying lighting and driving conditions. Experimental results demonstrated that the chosen scale factor for image down-sampling provided an optimal trade-off between processing speed and detection accuracy. The application of BHE improved face detection accuracy from 92% to 94% under extreme lighting conditions. The algorithm successfully estimated PERCLOS values in real-time, with a threshold of 15% used to trigger voice alarms for drowsy drivers. The system proved robust against actual driving dynamics, including head rotations and illumination changes, although it did not account for drivers wearing spectacles. The significance of this work lies in its successful integration of multiple computer vision techniques into a single, real-time embedded system suitable for automotive environments. By addressing key challenges such as illumination invariance and head rotation compensation, the proposed system offers a non-invasive, cost-effective solution for driver monitoring. The validation against EEG signals and real-world testing confirms the reliability of PERCLOS as a drowsiness indicator, contributing to the development of safer, automated driver assistance systems.

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Topics

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

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• Methodological Resource: tool software