Authors:
Sébastien Piérard
and
Marc Van Droogenbroeck
Affiliation:
University of Liège, Belgium
Keyword(s):
Human, Silhouette, Orientation, Machine learning, Regression.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Image Understanding
;
Pattern Recognition
;
Shape Representation
;
Software Engineering
;
Video Analysis
Abstract:
Estimating the orientation of the observed person is a crucial task for home entertainment, man-machine interaction,
intelligent vehicles, etc. This is possible but complex with a single camera because it only provides
one side view. To decrease the sensitivity to color and texture, we use the silhouette to infer the orientation.
Under these conditions, we show that the only intrinsic limitation is to confuse the orientation q with the supplementary
angle (that is 180º - q), and that the shape descriptor must distinguish between mirrored images.
In this paper, the orientation estimation is expressed and solved in the terms of a regression problem and supervised
learning. In our experiments, we have tested and compared 18 shape descriptors; the best one achieves
a mean error of 5:24º. However, because of the intrinsic limitation mentioned above, the range of orientations
is limited to 180º. Our method is easy to implement and outperforms existing techniques.