Estimating driver head pose using steerable pyramid and probabilistic learning Online publication date: Sat, 03-Oct-2015
by Nawal Alioua; Aouatif Amine; Abdelaziz Bensrhair; Mohammed Rziza
International Journal of Computational Vision and Robotics (IJCVR), Vol. 5, No. 4, 2015
Abstract: In this paper, we propose a driver head pose estimator based on steerable pyramid transform and probabilistic learning. The steerable pyramid is used to construct a head appearance template for each considered head orientation. Then, we learn the parameters of likelihood function from a training set with a probabilistic approach. To estimate the pose of a new head image, we first apply the steerable pyramid to extract its feature vector and then the maximal value of the likelihood function computed between this vector and all pose templates are retained. We perform several tests on public Pointing '04 database to optimise the parameters of steerable pyramid, which allows to make a compromise between the accuracy and processing time. Then, we apply the optimised head pose estimator on real video sequence representing a driver in diverse attention levels. We demonstrated that our system performs a good detection of driver inattention level.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Vision and Robotics (IJCVR):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook