Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580) (2000)
Mar. 26, 2000 to Mar. 30, 2000
Fernando de la Torre , Universitat Ramon LLull
Yaser Yacoob , University of Maryland at College Park
Larry Davis , University of Maryland at College Park
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.
Y. Yacoob, F. d. Torre and L. Davis, "A Probabilistic Framework for Rigid and Non-Rigid Appearance Based Tracking and Recognition," Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580)(FG), Grenoble, France9, 2000, pp. 491.