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Online learning and fusion of orientation appearance models for robust rigid object tracking

Marras, Ioannis; Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja


Ioannis Marras

Georgios Tzimiropoulos

Stefanos Zafeiriou

Maja Pantic


We introduce a robust framework for learning and fusing of orientation appearance models based on both texture and depth information for rigid object tracking. Our framework fuses data obtained from a standard visual camera and dense depth maps obtained by low-cost consumer depth cameras such as the Kinect. To combine these two completely different modalities, we propose to use features that do not depend on the data representation: angles. More specifically, our framework combines image gradient orientations as extracted from intensity images with the directions of surface normals computed from dense depth fields. We propose to capture the correlations between the obtained orientation appearance models using a fusion approach motivated by the original Active Appearance Models (AAMs). To incorporate these features in a learning framework, we use a robust kernel based on the Euler representation of angles which does not require off-line training, and can be efficiently implemented online. The robustness of learning from orientation appearance models is presented both theoretically and experimentally in this work. This kernel enables us to cope with gross measurement errors, missing data as well as other typical problems such as illumination changes and occlusions. By combining the proposed models with a particle filter, the proposed framework was used for performing 2D plus 3D rigid object tracking, achieving robust performance in very difficult tracking scenarios including extreme pose variations.


Marras, I., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2014). Online learning and fusion of orientation appearance models for robust rigid object tracking. Image and Vision Computing, 32(10), 707-727. doi:10.1016/j.imavis.2014.04.017

Journal Article Type Article
Acceptance Date Apr 5, 2014
Online Publication Date May 20, 2014
Publication Date Oct 14, 2014
Deposit Date Jan 29, 2016
Publicly Available Date Jan 29, 2016
Journal Image and Vision Computing
Print ISSN 0262-8856
Electronic ISSN 0262-8856
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 32
Issue 10
Pages 707-727
Keywords Rigid object tracking; Fusion of orientation appearance models; Subspace learning; Online learning; Face analysis; RGB-D
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Copyright Statement Copyright information regarding this work can be found at the following address:
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