Visual tracking of known three-dimensional objects
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Using the Active Appearance Algorithm for Face and Facial Feature Tracking
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient, Robust and Accurate Fitting of a 3D Morphable Model
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Active Appearance Models Revisited
International Journal of Computer Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Real-time combined 2D+3D active appearance models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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This paper presents a method for tracking the 3D pose of rigid objects. The proposed method is a 3D extension of the appearance-based approach called Active Appearance Models (AAM). Here, the 3D shape of the object and the geometry of the camera are added as part of the minimizing parameters of the AAM algorithm in order to determine the full 6 degree-of-freedom (DOF) pose of the object. This work is a twofold, major improvement of our previous work: First by applying the inverse compositional algorithm to the image alignment phase; and second, by incorporating the image gradient information into the same image alignment formulation. Both improvements make the method not only more time efficient, but they also increase the tracking accuracy, especially when the object is not rich in texture. Moreover, since our method is appearance-based, it does not require any customized feature extractions, which also translates into a more flexible alternative to situations with cluttered background, complex and irregular features, etc. The proposed method is compared with our previous work and with a previously developed algorithm using a geometric-based approach.