A Parzen classifier with an improved robustness against deviations between training and test data
Pattern Recognition Letters
Comparison of edge detectors: a methodology and initial study
Computer Vision and Image Understanding
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Turn-Intent Analysis Using Body Pose for Intelligent Driver Assistance
IEEE Pervasive Computing
Experimental study for the comparison of classifier combination methods
Pattern Recognition
Multi-spectral and multi-perspective video arrays for driver body tracking and activity analysis
Computer Vision and Image Understanding
Gait classification in children with cerebral palsy by Bayesian approach
Pattern Recognition
Classification of cerebral palsy gait by Kernel Fisher Discriminant Analysis
International Journal of Hybrid Intelligent Systems - Computational Models for Life Sciences
Vision-based infotainment user determination by hand recognition for driver assistance
IEEE Transactions on Intelligent Transportation Systems
Individual attribute prior setting methods for naïve Bayesian classifiers
Pattern Recognition
Modeling and Recognition of Driving Behavior Based on Stochastic Switched ARX Model
IEEE Transactions on Intelligent Transportation Systems
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Reports of traffic accidents show that a considerable percentage of the accidents are caused by human factors. Human-centric driver assistance systems, with integrated sensing, processing and networking, aim to find solutions to this problem and other relevant issues. The key technology in such systems is the capability to automatically understand and characterize driver behaviors. In this paper, we propose a novel, efficient feature extraction approach for driving postures from a video camera, which consists of Homomorphic filter, skin-like regions segmentation, canny edge detection, connected regions detection, small connected regions deletion and spatial scale ratio calculation. With features extracted from a driving posture dataset we created at Southeast University (SEU), holdout and cross-validation experiments on driving posture classification are then conducted using Bayes classifier. Compared with a number of commonly used classification methods including naive Bayes classifier, subspace classifier, linear perception classifier and Parzen classifier, the holdout and cross-validation experiments show that the Bayes classifier offers better classification performance than the other four classifiers. Among the four predefined classes, i.e., grasping the steering wheel, operating the shift gear, eating a cake and talking on a cellular phone, the class of talking on a cellular phone is the most difficult to classify. With Bayes classifier, the classification accuracies of talking on a cellular phone are over 90 % in holdout and cross-validation experiments, which shows the effectiveness of the proposed feature extraction method and the importance of Bayes classifier in automatically understanding and characterizing driver behaviors towards human-centric driver assistance systems.