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SUMMARY:Tracking intentionality using behavioural models and Bayesian meth
 ods.  - Prof. Simon Godsill\, CUED
DTSTART:20161020T140000Z
DTEND:20161020T150000Z
UID:TALK68624@talks.cam.ac.uk
CONTACT:Prof. Ramji Venkataramanan
DESCRIPTION:In this talk I will describe recent methods and applications f
 or high-level inference and tracking of multiple object\, groups and netwo
 rks\, by incorporation of behavioural interactions and unobserved intent i
 nto the dynamical models. The idea here is to extend the standard multiple
  object tracking paradigm to one in which we may automatically learn dynam
 ic interactions between those objects\, as well as infer possible intentio
 nalities of the objects. Our models are based on principles from animal be
 havioural analysis in which objects follow patterns of behaviour based loo
 sely upon what their neighbours in the group are doing\, and upon the (unk
 nown) intentionality of the group\, for example its final destination. We 
 may also learn more complex interactions such as whether one member of the
  group is a `leader' of the dynamics and how the objects are split between
  different groupings. Models are typically formulated in continuous time\,
  and inference is carried out on-line using numerical Bayesian filtering s
 trategies\, implemented with state of the art methods such as particle fil
 ters and Markov chain Monte Carlo. Applications will be presented from the
  areas of vehicle tracking\, wild animal pack hunting behaviour analysis\,
  financial time series\, and finally applications in User Interfaces for a
 utomobiles in which the task is to determine accurately and rapidly the in
 tended icon a user is pointing at on a screen\, based on the trajectory of
  hand motion near to the screen\, and in the presence of disturbances from
  suspension and road surface.
LOCATION:LR4\, Cambridge University Engineering Department
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