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A box particle filter method for tracking multiple extended objects (2018)
Journal Article
De Freitas, A., Mihaylova, L., Gning, A., Schikora, M., Ulmke, M., Angelova, D., & Koch, W. (2019). A box particle filter method for tracking multiple extended objects. IEEE Transactions on Aerospace and Electronic Systems, 55(4), 1640 - 1655. https://doi.org/10.1109/TAES.2018.2874147

Extended objects generate a variable number of multiple measurements. In contrast with point targets, extended objects are characterized with their size or volume, and orientation. Multiple object tracking is a notoriously challenging problem due to... Read More about A box particle filter method for tracking multiple extended objects.

Analysis of the EPSRC Principles of Robotics in regard to key research topics (2017)
Journal Article
Gning, A., Davis, D. N., Cheng, Y., & Robinson, P. (2017). Analysis of the EPSRC Principles of Robotics in regard to key research topics. Connection Science, 29(3), 249-253. https://doi.org/10.1080/09540091.2017.1323456

© 2017 Informa UK Limited, trading as Taylor & Francis Group. In this paper, we review the five rules published in EPSRC Principles of Robotics with a specific focus on future robotics research topics. It is demonstrated through a pictorial represe... Read More about Analysis of the EPSRC Principles of Robotics in regard to key research topics.

Autonomous crowds tracking with box particle filtering and convolution particle filtering (2016)
Journal Article
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

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 th... Read More about Autonomous crowds tracking with box particle filtering and convolution particle filtering.

Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking (2013)
Journal Article
Mihaylova, L., Carmi, A. Y., Septier, F., Gning, A., Pang, S. K., & Godsill, S. (2014). Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking. Digital Signal Processing, 25, 1-16. https://doi.org/10.1016/j.dsp.2013.11.006

This work presents the current state-of-the-art in techniques for tracking a number of objects moving in a coordinated and interacting fashion. Groups are structured objects characterized with particular motion patterns. The group can be comprised of... Read More about Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking.

Box-particle probability hypothesis density filtering (2013)
Journal Article
Schikora, M., Gning, A., Mihaylova, L., Cremers, D., & Koch, W. (2014). Box-particle probability hypothesis density filtering. IEEE Transactions on Aerospace and Electronic Systems, 50(3), 1660-1672. https://doi.org/10.1109/taes.2014.120238

This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able to track multiple targets and estimates the unknown number of targets. Furthermore, it is... Read More about Box-particle probability hypothesis density filtering.


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