Temporal uncertainty reasoning networks for evidence fusion with applications to object detection and tracking

In this paper, we present a temporal uncertainty-based inferencing paradigm for sensor networks. Multiple sensors observe a phenomenon and then exchange their probability estimates (for the occurrence of an event) with each other. Each node in the network fuses the evidence in such received messages, and computes the probability of occurrence of the relevant event. We develop and apply a temporal relevance decay model that accounts for the possibility that some observations lose their relevance or importance with the passage of time. As an illustrative example, this model is applied to the problems of object detection and tracking using multiple sensors with varying degrees of reliability.