A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A probabilistic framework for modeling and real-time monitoring human fatigue
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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.