Garcia-Fernandez, Angel 
ORCID: 0000-0002-6471-8455 and Xiao, Jimin
(2023)
Trajectory Poisson multi-Bernoulli mixture filter for
traffic monitoring using a drone.
IEEE Transactions on Vehicular Technology, 73 (1).
pp. 402-413.
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Abstract
This article proposes a multi-object tracking (MOT) algorithm for traffic monitoring using a drone equipped with optical and thermal cameras. Object detections on the images are obtained using a neural network for each type of camera. The cameras are modelled as direction-of-arrival (DOA) sensors. Each DOA detection follows a von-Mises Fisher distribution, whose mean direction is obtain by projecting a vehicle position on the ground to the camera. We then use the trajectory Poisson multi-Bernoulli mixture filter (TPMBM), which is a Bayesian MOT algorithm, to optimally estimate the set of vehicle trajectories. We have also developed a parameter estimation algorithm for the measurement model. We have tested the accuracy of the resulting TPMBM filter in synthetic and experimental data sets.
| Item Type: | Article |
|---|---|
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 01 Sep 2023 08:03 |
| Last Modified: | 14 Mar 2024 17:24 |
| DOI: | 10.1109/TVT.2023.3310742 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3172483 |
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