Estimation of Human Orientation based on Silhouettes and Machine Learning Principles

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 θ with the supplementary angle (that is 180°−θ), 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 5.24°. However, because of the intrinsic limitation mentioned above, the range of orientations is limited to 180°. Our method is easy to implement and outperforms existing techniques.