Absolute Head Pose Estimation From Overhead Wide-Angle Cameras
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Robust head tracking using 3D ellipsoidal head model in particle filter
Pattern Recognition
Pose Robust Face Tracking by Combining Active Appearance Models and Cylinder Head Models
International Journal of Computer Vision
Real-time face tracking and pose estimation with partitioned sampling and relevance vector machine
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Tracking by means of geodesic region models applied to multidimensional and complex medical images
Computer Vision and Image Understanding
3D motion segmentation using intensity trajectory
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Vector quantization segmentation for head pose estimation
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Robust 3D head tracking and its applications
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Real-time 3D head tracking under rapidly changing pose, head movement and illumination
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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We present a generic and robust method for model-based global 3D head pose estimation in monocular and non-calibrated video sequences. The proposed method relies on a 3D/2D matching between 2D image features estimated throughout the sequence and 3D object features of a generic head model. Specifically, it combines motion and texture features in an iterative optimization procedure based on the downhill simplex algorithm. A proper initialization of the pose parameters, based on a block matching procedure, is performed at each frame in order to take into account large amplitude motions. For the same reason, we have developed a non-linear optical flow-based interpolation algorithm for increasing the frame rate. Experiments demonstrate that this method is stable over extended sequences including large head motions, occlusions, various head postures and lighting variations. The estimation accuracy is related to the head model, as established by using an ellipsoidal model and an ad hoc synthesized model. The proposed method is general enough to be applied to other tracking applications.