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Autonomous crowds tracking with box particle filtering and convolution particle filtering

De Freitas, Allan; Mihaylova, Lyudmila; Gning, Amadou; Angelova, Donka; Kadirkamanathan, Visakan


Allan De Freitas

Lyudmila Mihaylova

Amadou Gning

Donka Angelova

Visakan Kadirkamanathan


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.


De Freitas, A., Mihaylova, L., Gning, A., Angelova, D., & Kadirkamanathan, V. (2016). Autonomous crowds tracking with box particle filtering and convolution particle filtering. Automatica : the journal of IFAC, the International Federation of Automatic Control, 69, 380-394.

Journal Article Type Article
Acceptance Date Feb 26, 2016
Online Publication Date Mar 25, 2016
Publication Date 2016-07
Deposit Date Jan 2, 2019
Publicly Available Date Jan 3, 2019
Journal Automatica
Print ISSN 0005-1098
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 69
Pages 380-394
Keywords Control and Systems Engineering; Electrical and Electronic Engineering
Public URL
Publisher URL


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Copyright Statement
This work is licensed under a Creative Commons Attribution 4.0 International License.

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