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
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
Emotion recognition using facial expressions with active appearance models
HCI '08 Proceedings of the Third IASTED International Conference on Human Computer Interaction
Multiple faces tracking using motion prediction and IPCA in particle filters
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Robust face tracking using motion prediction in adaptive particle filters
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Tracking a face and its facial features in a video sequence is a challenging problem in computer vision. In this view, we propose a stochastic tracking system based on a particle- filtering scheme. In this paradigm, the unobserved state includes global face pose and appearance parameters coding both shape and texture information of the face. The adopted observations distribution is derived from an Active Appearance Model (AAM). The transition distribution and the particles number are adaptive in the sense that they are guided by an AAM deterministic search. This optimization stage adjusts the explored area of the state space to the quality of the prediction and enables a substantial gain in computing time. The observation model uses a robust distance measure in order to account for occlusions. Experiments on real video show encouraging results.