Autonomous Crowds Tracking with Box Particle Filtering and Convolution Particle Filtering

TL;DR

This paper introduces two novel particle filtering methods, box and convolution, for autonomous crowd tracking that effectively handle measurement uncertainties and data association challenges in complex scenarios.

Contribution

It develops and compares two innovative filtering approaches, deriving a likelihood function for box particle filtering and implementing an adaptive convolution particle filter.

Findings

Both filters accurately track crowds in realistic scenarios.

The methods effectively resolve data association problems.

Performance comparison shows advantages of each approach.

Abstract

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.