Research Article Driving Posture Recognition by Joint Application of Motion History Image and Pyramid Histogram of Oriented Gradients Chao Yan, 1 Frans Coenen, 2 and Bailing Zhang 1 1 Department of Computer Science and Sotware Engineering, Xi’an Jiaotong-Liverpool University, SIP, Suzhou 215123, China 2 Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK Correspondence should be addressed to Bailing Zhang; bailing.zhang@xjtlu.edu.cn Received 3 August 2013; Accepted 30 October 2013; Published 28 January 2014 Academic Editor: Aboelmagd Noureldin Copyright © 2014 Chao Yan et al. his 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 ield of intelligent transportation system (ITS), automatic interpretation of a driver’s behavior is an urgent and challenging topic. his 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 let proile. he driving actions, including operating the shit lever, talking on a cell phone, eating, and smoking, are irst decomposed into a number of predeined action primitives, that is, interaction with shit lever, operating the shit lever, interaction with head, and interaction with dashboard. A global grid-based representation for the action primitives was emphasized, which irst generate the silhouette shape from motion history image, followed by application of the pyramid histogram of oriented gradients (PHOG) for more discriminating characterization. he random forest (RF) classiier was then exploited to classify the action primitives together with comparisons to some other commonly applied classiiers such as NN, multiple layer perceptron, and support vector machine. Classiication accuracy is over 94% for the RF classiier in holdout and cross-validation experiments on the four manually decomposed driving actions. 1. Introduction In China, the number of personal-use automobiles has con- tinued to grow at a rapid rate, reaching the number 120,890,000 in 2012. According to the World Health Orga- nization (WHO), there is an estimated number of 250,000 deaths due to road accidents every year, making it the leading cause of death for people aged 14 to 44. Unsafe and dangerous driving accounts for the death of more than one million lives and over 50 million serious injuries worldwide each year [1]. he WHO also estimates that traic accidents cost the Chinese economy over $21 billion each year. One of key contributing factors is reckless driving [1]. It is a proven fact that drivers who are reaching for an object such as a cell- phone are three times more likely to be involved in a motor vehicle accident, while actually using a cell-phone increases the risks to six times as likely. In order to reduce unsafe driving behaviors, one of the proposed solutions is to develop a camera-based system to monitor the activities of drivers. his is particularly relevant for long-distance truck and bus drivers. For example, in many countries, including China, it is illegal for drivers to be using their cell-phone whilst driving. Drivers who violate the restriction face civil penalties. However, how to automatically distinguish between safe and unsafe driving actions is not a trivial technical issue. Since most commercial drivers operate alone, most of their driving behaviors are not directly observ- able by others. Such barriers will disappear when in-vehicle technologies become available to observe driver behaviors. An emerging technology that has attracted wide attention is the development of driver alertness monitoring systems which aims at measuring driver status and performance to provide in-vehicle warnings and feedback to drivers. Truck and bus leet managers are particularly interested in such systems to acquire sound safety management. hey can regularly track their driver outcomes and provide prevention of crashes, incidents, and violations. Vision-based driving activity monitoring is closely related to human action recognition (HAR), which is an important area of computer vision research and applications. he goal Hindawi Publishing Corporation International Journal of Vehicular Technology Volume 2014, Article ID 719413, 11 pages http://dx.doi.org/10.1155/2014/719413