Sequential inference for dynamically evolving groups of objects
Seminar Room 1, Newton Institute
In this talk I will describe recent work on tracking for groups of objects. The aim of the process is to infer evolving groupings of moving objects over time, including group affiliations and individual object states. Behaviour of group objects is modelled using interacting multiple object models, in which individuals attempt stochastically to adjust their behaviour to be `similar' to that of other objects in the same group; this idea is formalised as a multi-dimensional stochastic differential equation for group object motion. The models are estimated algorithmically using sequential Markov chain Monte Carlo approximations to the filtering distributions over time, allowing for more complex modelling scenarios than the more familiar importance-sampling based Monte Carlo filtering schemes. Examples will be presented from GMTI data trials for multiple vehicle motion.
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