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The Pixel-Based Adaptive Segmenter with Confidence Measurement (PBASCM) is proposed for vehicle detection in complex urban traffic scenes to efficiently address deficiencies of the background subtraction model, which is easily contaminated by slow-moving or temporarily stopped vehicles. The background is modeled based on the history of recently observed pixel values and each pixel in the background model is assigned a confidence measurement based on the current traffic state. The foreground decision depends on an adaptive threshold, whereas the background model is updated based on the current traffic state and whether the corresponding pixel point is in the confidence period. Using real-world urban traffic videos, the overall results of detection accuracy analyses demonstrated that PBASCM achieved better performance in both qualitative and quantitative evaluations, compared with other state-of-the-art methods. PBASCM can accurately detect slowmoving or temporarily stopped vehicles, and the similarity and F-measure results for PBASCM were 0.839 and 0.912 higher, respectively, than those obtained by other methods in a traffic light sequence during the daytime. Thus, our experimental results demonstrate that PBASCM is effective and suitable for real-time vehicle detection in complex urban traffic scenes.

To develop the human-centric driver fatigue monitoring system for automatic understanding and charactering of driver's conditions, a novel, efficient feature extraction approach, named Local Multiresolution Derivative Pattern (LMDP), is proposed to describe the driver's fatigue expression images, and the Intersection Kernel Support Vector Machines classifier is then exploited to recognize three pre-defined classes of fatigue expressions, i.e., awake expressions, moderate fatigue expressions and severe fatigue expressions. With features extracted from a fatigue expressions dataset created at Southeast University, the holdout and cross-validation experiments on fatigue expressions classification are conducted by the Intersection Kernel Support Vector Machines classifier, compared with three commonly used classification methods including the k-nearest neighbor classifier, the multilayer perception classifier and the dissimilarity-based classifier. The experimental results of holdout and cross-validation showed that LMDP offers the better performance than Local Derivative Pattern, and the second order LMDP exceeds other order LMDP. With the second order LMDP and the Intersection Kernel Support Vector Machines classifier, the classification accuracies of the severe fatigue are over 90 in the holdout and cross-validation experiments, thus demonstrating the effectiveness of the proposed feature extraction method in automatically understanding the driver's conditions towards the human-centric driver fatigue monitoring system.

In order to develop Human-centered Driver Assistance Systems (HDAS), an efficient Combined Feature (CF) extraction approach from Contourlet Transform (CT) and Edge Orientation Histogram (EOH) is proposed for vehicle driving posture descriptions. A Random Subspace Ensemble (RSE) of Intersection Kernel Support Vector Machines (IKSVMs) is then exploited as the base classifier. Four testing driving postures are grasping the steering wheel, operating the shift lever, eating a cake, and talking on a cellar phone. On a dedicated Southeast University Driving Posture (SEU-DP) Database, the holdout and cross-validation experiments were conducted. The experimental results show that the proposed CF-RSE approach outperforms single Contourlet-IKSVM, EOH-IKSVM recognition strategies. With CF-RSE, the average classification accuracies of four driving posture classes are over 90%. Among the four classes of driving postures, the class of grasping the steering wheel is the most difficult to recognize and the proposed approach achieved over 85% accuracy in both experiments. These encouraging results show that the proposed CF-RSE approach is effective and hence has great promises in developing a successful HDAS.