Name
Abstract | ||
|---|---|---|
AbstractIn 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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1504/IJCVR.2015.072194 | Periodicals |
Field | DocType | Volume |
Steerable pyramid,Computer vision,Feature vector,Likelihood function,Pattern recognition,Computer science,Advanced driver assistance systems,Feature extraction,Artificial intelligence,Probabilistic logic,Active safety,Estimator | Journal | 5 |
Issue | ISSN | Citations |
4 | 1752-9131 | 1 |
PageRank | References | Authors |
0.36 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nawal Alioua | 1 | 11 | 2.32 |
| Aouatif Amine | 2 | 85 | 9.29 |
| Abdelaziz Bensrhair | 3 | 81 | 16.67 |
| Mohammed Rziza | 4 | 89 | 18.32 |