Dynamic Bayesian Network modeling for self- and cross-correcting tracking
We present a generic formulation of self- and cross-correcting Bayesian trackers using a Dynamic Bayesian Network. Correction operations in a tracker such as parameter tuning, model updates and re-initialization are represented using hidden variables together with the target state and measurement variables in the Dynamic Bayesian network model. The representation allows one to model different self- and cross-correcting tracking frameworks under the same formulation and facilitates comparison and the design of new trackers. The proposed model is demonstrated with three state-of-the-art trackers that are based on different principles to implement online correction of target tracking.