Driving posture recognition by a hierarchal classification system with multiple features

This paper presents a novel system for vision-based driving posture recognition. The driving posture dataset was prepared by a side-mounted camera looking at a driver's left profile. After pre-processing for illumination variations, eight action classes of constitutive components of the driving activities were segmented, including normal driving, operating a cell phone, eating and smoking. A global grid-based representation for the action sequence was emphasized, which featured two consecutive steps. Step 1 generates a motion descriptive shape based on a motion frequency image(MFI), and step 2 applies the pyramid histogram of oriented gradients (PHOG) for more discriminating characterization. A three level hierarchal classification system is designed to overcome the difficulties of some overlapping classes. Four commonly applied classifiers, including k-nearest neighbor(KNN), random forest (RF), support vector machine(SVM) and multiple layer perceptron (MLP), are evaluated in each level. The overall classification accuracy is over 87.2% for the eight classes of driving actions by the proposed classification system.