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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

Authors

Allan De Freitas

Lyudmila Mihaylova

Amadou Gning

Donka Angelova

Visakan Kadirkamanathan



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.

Journal Article Type Article
Publication Date 2016-07
Journal Automatica
Print ISSN 0005-1098
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 69
Pages 380-394
APA6 Citation 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. doi:10.1016/j.automatica.2016.03.009
DOI https://doi.org/10.1016/j.automatica.2016.03.009
Keywords Control and Systems Engineering; Electrical and Electronic Engineering
Publisher URL https://www.sciencedirect.com/science/article/pii/S0005109816300887?via%3Dihub

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



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