Driving Posture Recognition by Joint Application of Motion History Image and Pyramid Histogram of Oriented Gradients

Abstract

In the field of intelligent transportation system (ITS), automatic interpretation of a driver’s behavior is an urgent and challenging topic. This paper studies vision-based driving posture recognition in the human action recognition framework. A driving action dataset was prepared by a side-mounted camera looking at a driver’s left profile. The driving actions, including operating the shift lever, talking on a cell phone, eating, and smoking, are first decomposed into a number of predefined action primitives, that is, interaction with shift lever, operating the shift lever, interaction with head, and interaction with dashboard. A global grid-based representation for the action primitives was emphasized, which first generate the silhouette shape from motion history image, followed by application of the pyramid histogram of oriented gradients (PHOG) for more discriminating characterization. The random forest (RF) classifier was then exploited to classify the action primitives together with comparisons to some other commonly applied classifiers such as kNN, multiple layer perceptron, and support vector machine. Classification accuracy is over 94% for the RF classifier in holdout and cross-validation experiments on the four manually decomposed driving actions.