  Autonomous systems such as Unmanned Aerial Vehicles (UAVs) need to be able to
recognise and track crowds of people, e.g. for rescuing and surveillance
purposes. Large groups generate multiple measurements with uncertain origin.
Additionally, often the sensor noise characteristics are unknown but
measurements are bounded within certain intervals. In this work we propose two
solutions to the crowds tracking problem - with a box particle filtering
approach and with a convolution particle filtering approach. The developed
filters can cope with the measurement origin uncertainty in an elegant way,
i.e. resolve the data association problem. For the box particle filter (PF) we
derive a theoretical expression of the generalised likelihood function in the
presence of clutter. An adaptive convolution particle filter (CPF) is also
developed and the performance of the two filters is compared with the standard
sequential importance resampling (SIR) PF. The pros and cons of the two filters
are illustrated over a realistic scenario (representing a crowd motion in a
stadium) for a large crowd of pedestrians. Accurate estimation results are
achieved.
