A Comparison of Features for Regression-based Driver Head Pose Estimation under Varying Illumination Conditions

Walger, D.J.; Breckon, T.P.; Gaszczak, A.; Popham, T.

Abstract

Head pose estimation provides key information about driver activity and awareness. Prior comparative studies are limited to temporally consistent illumination conditions under the assumption of brightness constancy. By contrast the illumination conditions inside a moving vehicle vary considerably with environmental conditions. In this study we present a base comparison of three features for head pose estimation, via support vector machine regression, based on Histogram of Oriented Gradient (HOG) features, Gabor filter responses and Active Shape Model (ASM) landmark features. These, reputedly illumination invariant, are presented through a common face localization framework from which we estimate driver head pose in two degrees-of-freedom and compare against a baseline approach for recovering head pose via weak perspective geometry. Evaluation is performed over a number of invehicle sequences, exhibiting uncontrolled illumination variation, in addition to ground truth data-sets, with controlled illumination changes, upon which we achieve a minimal ∼12° and ∼15° mean error in pitch and yaw respectively via ASM landmark features.

Citation

Walger, D., Breckon, T., Gaszczak, A., & Popham, T. (2014, November). A Comparison of Features for Regression-based Driver Head Pose Estimation under Varying Illumination Conditions. Presented at Proc. International Workshop on Computational Intelligence for Multimedia Understanding

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Proc. International Workshop on Computational Intelligence for Multimedia Understanding
• Publication Date: 2014
• Deposit Date: Dec 9, 2014
• Publicly Available Date: Feb 4, 2015
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1-5
• Book Title: Proc. International Workshop on Computational Intelligence for Multimedia Understanding
• DOI: https://doi.org/10.1109/IWCIM.2014.7008805
• Keywords: head pose, driver head tracking, gaze tracking, pose estimation regression
• Public URL: https://durham-repository.worktribe.com/output/1154715
• Publisher URL: https://breckon.org/toby/publications/papers/walger14headpose.pdf
• Related Public URLs: http://www.durham.ac.uk/toby.breckon/publications/papers/walger14headpose.pdf