A Probabilistic Framework for
Rigid and Non-Rigid Appearance 
based Tracking and Recognition
 (with  Y. Yacoob and L. Davis)
 

 
This paper describes an unified probabilistic framework for appearance based tracking of rigid and non-rigid objects.A spatio-temporal dependent shape/texture Eigenspace and mixture of diagonal gaussians are learned in a Hidden Markov Model(HMM) like structure to better constrain the model and for recognition purposes. Particle filtering is used to track the object while switching between different shape/texture models. This framework allows recognition and temporal segmentation of activities.  Additionally an automatic stochastic initialization is proposed, the number of states in the HMM are selected based on the Akaike Information Criterion and comparison with deterministic tracking for 2D models is discussed. Preliminary results of eye-tracking, lip-tracking and temporal segmentation of mouth events are presented.

 
 

   F. De la Torre, Y. Yacoob, L. Davis. 
A Probabilistic Framework for Rigid and Non-Rigid Appearance based Tracking and Recognition
Fourth IEEE International Conference on Automatic Face and Gesture Recognition. Grenoble 2000.
 (Postcript 1.7 MB) (pdf 0.36 Mb)
 

 

       
  Real Time Eye Tracking  
  Eye Tracking   (AVI+zip -7.27Mb)   
    Eye Tracking with changes in illumination (AVI+zip - 2.89 Mb  
    Eye Tracking with changes in view (AVI+zip - 4.09Mb  
         
  Lip Tracking and Expression Recognition  
     
    Coming soon...