Dynamic features based driver fatigue detection

Driver fatigue is an important reason for traffic accidents. To account for the temporal aspect of human fatigue, we propose a novel method based on dynamic features to detect fatigue from image sequences. First, global features are extracted from each image and concatenated into dynamic features. Then each feature is coded by the means of training samples, and weak classifiers are constructed on histograms of the coded features. Finally AdaBoost is applied to select the most critical features and establish a strong classifier for fatigue detection. The proposed method is validated under real-life fatigue conditions. The test data includes 600 image sequences with illumination and pose variations from thirty people's videos. Experiment results show the validity of the proposed method and the average recognition rate is 95.00% which is much better than the baselines.