A Probabilistic Framework for Joint Head Tracking and Pose Estimation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Rapid Pose Estimation of Mongolian Faces using Projective Geometry
AIPR '04 Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop
EM enhancement of 3D head pose estimated by point at infinity
Image and Vision Computing
Real-time elliptical head contour detection under arbitrary pose and wide distance range
Journal of Visual Communication and Image Representation
A LQR spatiotemporal fusion technique for face profile collection in smart camera surveillance
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Recognition of human head orientation based on artificial neural networks
IEEE Transactions on Neural Networks
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Covert-tracking robot refers to the mobile robot that not only can follow a human objective, but at the same time can control itself to keep away from the human's visual field. This paper proposes a head orientation estimation method based on probability model, which can help the robot to implement its covert behaviors. First, the elliptical head contour is tracked out by using a method based on quadrant arcs; then it is normalized into predefined size and is partitioned into 24 sub-areas. According to skin color model, an orientation probability model is built for each discrete angle. The final estimation is obtained by weighting each discrete angle, where the weight is calculated out by matching the current input with the model corresponding to the discrete angle. Experiment results confirm the method's good performance, strong robustness to different distance and high real time property.