Machine Learning
A Parzen classifier with an improved robustness against deviations between training and test data
Pattern Recognition Letters
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
A Two-Stage Robust Statistical Method for Temporal Registration from Features of Various Type
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Output-Sensitive Algorithms for Computing Nearest-Neighbour Decision Boundaries
Discrete & Computational Geometry
Turn-Intent Analysis Using Body Pose for Intelligent Driver Assistance
IEEE Pervasive Computing
Multi-spectral and multi-perspective video arrays for driver body tracking and activity analysis
Computer Vision and Image Understanding
3D Gaze Tracking and Analysis for Attentive Human Computer Interaction
FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
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
Modeling and prediction of driver behavior by foot gesture analysis
Computer Vision and Image Understanding
A general approach for analysis and application of discretemultiwavelet transforms
IEEE Transactions on Signal Processing
Modeling and Recognition of Driving Behavior Based on Stochastic Switched ARX Model
IEEE Transactions on Intelligent Transportation Systems
The application of multiwavelet filterbanks to image processing
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
Weighted Piecewise LDA for Solving the Small Sample Size Problem in Face Verification
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
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To develop Human-centric Driver Assistance Systems (HDAS) for automatic understanding and charactering of driver behaviors, an efficient feature extraction of driving postures based on Geronimo-Hardin-Massopust (GHM) multiwavelet transform is proposed, and Multilayer Perceptron (MLP) classifiers with three layers are then exploited in order to recognize four pre-defined classes of driving postures. With features extracted from a driving posture dataset created at Southeast University (SEU), the holdout and cross-validation experiments on driving posture classification are conducted by MLP classifiers, compared with the Intersection Kernel Support Vector Machines (IKSVMs), the k-Nearest Neighbor (kNN) classifier and the Parzen classifier. The experimental results show that feature extraction based on GHM multwavelet transform and MLP classifier, using softmax activation function in the output layer and hyperbolic tangent activation function in the hidden layer, offer the best classification performance compared to IKSVMs, kNN and Parzen classifiers. The experimental results also show that talking on a cellular phone is the most difficult one to classify among four predefined classes, which are 83.01% and 84.04% in the holdout and cross-validation experiments respectively. These results show the effectiveness of the feature extraction approach using GHM multiwavelet transform and MLP classifier in automatically understanding and characterizing driver behaviors towards Human-centric Driver Assistance Systems (HDAS).