Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Directional eigentemplate learning for sparse template tracker
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Real-time face tracking and recognition by sparse eigentracker with associative mapping to 3D shape
Image and Vision Computing
| Hi-index | 0.00 |
Automatic eigentemplate learning is discussed for a sparse template tracker. It is known that a sparse template tracker can effectively track a moving target using an eigentemplate when it is appropriately prepared for a motion class or for an illumination class. However, it has not been easy to prepare an eigentemplate automatically for any image sequences. This paper provides a feasible solution to this problem in the framework of sparse template tracking. In the learning phase, the sparse template tracker adaptively tracks a target object in a given image sequence when the first template is provided in the first image. By selecting a small number of representative and effective images, we can make up an eigentemplate by the principal component analysis. Once the eigentemplate learning is accomplished, the sparse template tracker can work with the eigentemplate instead of an adaptive template. Since the sparse eigentemplate tracker doesn't require any adaptive tracking, it can work more efficiently and effectively for image sequences in the class of learned appearance changes. Experimental results are provided for real-time face tracking when eigentemplates are learned for pose changes and for illumination changes, respectively.