4 research outputs found
Robust Multi-Object Tracking: A Labeled Random Finite Set Approach
The labeled random finite set based generalized multi-Bernoulli filter is a tractable analytic solution for the multi-object tracking problem. The robustness of this filter is dependent on certain knowledge regarding the multi-object system being available to the filter. This dissertation presents techniques for robust tracking, constructed upon the labeled random finite set framework, where complete information regarding the system is unavailable
Acoustic source localisation and tracking using microphone arrays
This thesis considers the domain of acoustic source localisation and tracking in an indoor environment.
Acoustic tracking has applications in security, human-computer interaction, and the
diarisation of meetings. Source localisation and tracking is typically a computationally expensive
task, making it hard to process on-line, especially as the number of speakers to track increases.
Much of the literature considers single-source localisation, however a practical system
must be able to cope with multiple speakers, possibly active simultaneously, without knowing
beforehand how many speakers are present. Techniques are explored for reducing the computational
requirements of an acoustic localisation system. Techniques to localise and track
multiple active sources are also explored, and developed to be more computationally efficient
than the current state of the art algorithms, whilst being able to track more speakers.
The first contribution is the modification of a recent single-speaker source localisation technique,
which improves the localisation speed. This is achieved by formalising the implicit assumption
by the modified algorithm that speaker height is uniformly distributed on the vertical
axis. Estimating height information effectively reduces the search space where speakers have
previously been detected, but who may have moved over the horizontal-plane, and are unlikely
to have significantly changed height. This is developed to allow multiple non-simultaneously
active sources to be located. This is applicable when the system is given information from a
secondary source such as a set of cameras allowing the efficient identification of active speakers
rather than just the locations of people in the environment.
The next contribution of the thesis is the application of a particle swarm technique to significantly
further decrease the computational cost of localising a single source in an indoor environment,
compared the state of the art. Several variants of the particle swarm technique are
explored, including novel variants designed specifically for localising acoustic sources. Each
method is characterised in terms of its computational complexity as well as the average localisation
error. The techniques’ responses to acoustic noise are also considered, and they are
found to be robust.
A further contribution is made by using multi-optima swarm techniques to localise multiple
simultaneously active sources. This makes use of techniques which extend the single-source
particle swarm techniques to finding multiple optima of the acoustic objective function. Several
techniques are investigated and their performance in terms of localisation accuracy and computational
complexity is characterised. Consideration is also given to how these metrics change
when an increasing number of active speakers are to be localised.
Finally, the application of the multi-optima localisation methods as an input to a multi-target
tracking system is presented. Tracking multiple speakers is a more complex task than tracking
single acoustic source, as observations of audio activity must be associated in some way with
distinct speakers. The tracker used is known to be a relatively efficient technique, and the nature
of the multi-optima output format is modified to allow the application of this technique to the
task of speaker tracking