Bayesian network for multiple hypothesis tracking

For a flexible camera-to-camera tracking of multiple objects we model the object’s behavior with a Bayesian network and combine it with the multiple hypothesis framework that associates observations with objects. Bayesian networks offer a possibility to factor complex, joint distributions into a product of intuitive conditional densities describing and predicting the object’s path. Yet, these models do not distinguish unambiguously between the object’s observations. The resulting uncertainty is explored by a multiple hypothesis evaluation. The paper provides experimental evidence of the performance of the proposed method.