Adaptive learning-based temporal tracker for 3D head shape models
Tan DJ, Navab N, Tombari F (2017)
Publication Type: Conference contribution
Publication year: 2017
Publisher: BMVA Press
Conference Proceedings Title: British Machine Vision Conference 2017, BMVC 2017
Event location: London, GBR
ISBN: 190172560X
Abstract
We propose a learning-based 3D temporal tracker that estimates the orientation and location of the head in the 3D scene. The algorithm is based on random forest that learns the 6D pose from a class of head models. A unique attribute of our approach is the capacity to adapt the learned tracker for a specific user, i.e., after learning, the tracker can deform the shape of the learned model to a specific instance of the class in order to match the user’s head shape. To find the user’s head shape model, we use a fast calibration method to personalize the model for a specific user. As a consequence, this technique enhances the accuracy of the head pose estimation as the personalized model becomes more detailed, and tracks at 1.4 ms per frame using a single CPU core.
Involved external institutions
Germany (DE)How to cite
APA:
Tan, D.J., Navab, N., & Tombari, F. (2017). Adaptive learning-based temporal tracker for 3D head shape models. In British Machine Vision Conference 2017, BMVC 2017. London, GBR: BMVA Press.
MLA:
Tan, David Joseph, Nassir Navab, and Federico Tombari. "Adaptive learning-based temporal tracker for 3D head shape models." Proceedings of the 28th British Machine Vision Conference, BMVC 2017, London, GBR BMVA Press, 2017.
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