An Improved Fatigue Detection System Based on Behavioral Characteristics of Driver
URL: http://arxiv.org/abs/1709.05669v1
archive: archived pipeline: cataloged verified
Abstract
In recent years, road accidents have increased significantly. One of the major reasons for these accidents, as reported is driver fatigue. Due to continuous and longtime driving, the driver gets exhausted and drowsy which may lead to an accident. Therefore, there is a need for a system to measure the fatigue level of driver and alert him when he/she feels drowsy to avoid accidents. Thus, we propose a system which comprises of a camera installed on the car dashboard. The camera detect the driver's face and observe the alteration in its facial features and uses these features to observe the fatigue level. Facial features include eyes and mouth. Principle Component Analysis is thus implemented to reduce the features while minimizing the amount of information lost. The parameters thus obtained are processed through Support Vector Classifier for classifying the fatigue level. After that classifier output is sent to the alert unit.
Summary
Gupta, Aman, Shiva & Singh (2017, IEEE conference paper from IIT Dhanbad) propose a behavioral driver-fatigue detection system using a dashboard camera that observes facial features (eyes and mouth). Principal Component Analysis reduces feature dimensionality and a Support Vector Classifier categorizes drowsiness state, with classifier output routed to an alert unit. The authors position the system as subject-independent, calibration-free, and robust relative to physiological-signal approaches that require skin-contact sensors and to vehicle-dynamics approaches constrained by road and driving conditions.
Key finding
Combining facial-feature extraction (eyes, mouth) with PCA dimensionality reduction and SVM classification offers a calibration-free, behavior-based alternative to physiological sensors for driver-fatigue detection.
Methodology
modeling
Quality score: 5 / 5