EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Sparse Probabilistic Learning Algorithm for Real-Time Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Multiple Target Tracking by Appearance-Based Condensation Tracker using Structure Information
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Eigentemplate Learning for Sparse Template Tracker
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Head Pose Estimation in Computer Vision: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking 3d Pose of Rigid Object by Sparse Template Matching
ICIG '09 Proceedings of the 2009 Fifth International Conference on Image and Graphics
Memory-based particle filter for tracking objects with large variation in pose and appearance
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Automatic eigentemplate learning is discussed for a sparse template tracker. Using an eigentemplate learned from multiple sequences, a sparse template tracker can efficiently track a target that changes appearance. The present paper provides a feasible solution for eigentemplate learning when multiple image sequences are available. Two types of eigentemplates are compared in the present paper, namely, a single eigentemplate, and a set of directional eigentemplates. The single eigentemplate simply consists of all images learned from multiple sequences.On the other hand, directional eigentemplates are obtained by decomposing the single eigentemplate into three directions of the face poses. The sparse template tracker is also expanded to directional eigentemplates.Finally, the effectiveness of the provided solution is demonstrated in the learning and tracking experiments. The experimental results indicate that directional learning works well with small seed data,and that the directional eigentracker works better than the single eigentracker.