Table of Contents
International Journal of Vehicular Technology
Volume 2014 (2014), Article ID 719413, 11 pages
Research Article

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

1Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, SIP, Suzhou 215123, China
2Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK

Received 3 August 2013; Accepted 30 October 2013; Published 28 January 2014

Academic Editor: Aboelmagd Noureldin

Copyright © 2014 Chao Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 NN, 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.