Tracking using Bayesian inference with a two-layer graphical model

This paper introduces a new visual tracking technique combining particle filtering and Dynamic Bayesian Networks. The particle filter is utilized to robustly track an object in a video sequence and gain sets of descriptive object features. Dynamic Bayesian Networks use feature sequences to determine different motion patterns. A Graphical Model is introduced, which combines particle filter based tracking with Dynamic Bayesian Network-based classification. This unified framework allows for enhancing the tracking by adapting the dynamical model of the tracking process according to the classification results obtained from the Dynamic Bayesian Network. Therefore, the tracking step and classification step form a closed trackingclassification- tracking loop. In the first layer of the Graphical Model a particle filter is set up, whereas the second layer builds up the dynamical model of the particle filter based on the classification process of the Dynamic Bayesian Network.