An Improved Fatigue Detection System Based on Behavioral Characteristics of Driver
This addresses road safety by potentially reducing accidents caused by drowsy driving, but it is incremental as it builds on existing methods like PCA and SVM.
The paper tackles driver fatigue detection by using a camera to monitor facial features (eyes and mouth), applying PCA for feature reduction and SVM for classification, resulting in a system that alerts drivers to prevent accidents.
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.