Illumination invariant head pose estimation using single camera

Author

Nanda, Harsh ; FujiMura, Kikuo

Author_Institution

Dept. of Comput. Sci., Maryland Univ., College Park, MD, USA

fYear

2003

Firstpage

434

Lastpage

437

Abstract

A learning based approach is presented for a single camera based robust head pose estimation in highly cluttered and complex real world environments. The method makes effective use of a recently introduced real-time 3D depth sensing technology, resulting in illumination-invariant head pose estimation. The main target application of our work is to classify the focus of attention of an automobile driver as straight-ahead, towards-rear-view mirror, towards the dash board, etc. This type of information is expected to be useful in conjunction with other sensors to enable safer driving.

Keywords

automobiles; computer vision; image recognition; learning by example; neural nets; principal component analysis; real-time systems; 3D depth sensing technology; attention focus; automobile driver; complex real world environments; dash board; head pose estimation; illumination invariant; learning based approach; neural nets; principal component analysis; real time technology; rear view mirror; Cameras; Face detection; Focusing; Head; Infrared sensors; Layout; Lighting; Neural networks; Principal component analysis; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Vehicles Symposium, 2003. Proceedings. IEEE

Print_ISBN

0-7803-7848-2

Type

conf

DOI

10.1109/IVS.2003.1212950

Filename

1212950