High-Level Tracking Using Bayesian Context Fusion

This paper presents a Bayesian tracking approach that exploits various types of context information. The filtering accuracy and precision are improved by using uncertain information about (i) the constraints on the target mobility, (ii) environmental influences on the sensor performance and (iii) typical target behaviors. The approach combines particle filters with exact Bayesian networks. The overall process is equivalent to approximate inference on elaborate dynamic Bayesian networks that systematically capture non-trivial correlations between the estimated states of the dynamic processes, the associated observations and the various factors influencing the dynamic processes. The particle filter supports reasoning about continuous dynamic processes spanning large areas, while the Bayesian networks are used for the implementation of advanced sensor models and for the fusion of uncertain data on mobility constraints. The derivation of the solution is based on the decomposability principles of Bayesian networks. The approach is illustrated with the help of a challenging wildlife protection application. A set of qualitative experiments shows the improvement in tracking performance by considering the different types of context information.