Trajectory Poisson multi-Bernoulli mixture filter for traffic monitoring using a drone



Garcia-Fernandez, Angel 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
Uncontrolled Keywords: 4605 Data Management and Data Science, 46 Information and Computing Sciences, 40 Engineering, 4001 Aerospace Engineering, Bioengineering
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: 20 Jun 2024 16:26
DOI: 10.1109/TVT.2023.3310742
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3172483