Surveillance in wide-area spatial environments is characterised by complex spatial layouts, large state space, and the use of multiple cameras/sensors. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. This requirement is particularly suited to the layered dynamic probabilistic network (LDPN), a special type of dynamic probabilistic network. In this paper, we propose the use of LDPN as the integrated framework for tracking in wide-area environments. We illustrate, with the help of a synthetic tracking scenario, how the parameters of the LDPN can be estimated from training data, and then used to draw predictions and answer queries about unseen tracks at various levels of detail.
['Hung Hai Bui', 'Svetha Venkatesh', 'Geoff A. W. West']
A probabilistic framework for tracking in wide-area environments