Trajectory Poisson Multi-Bernoulli Mixture Filter for Traffic Monitoring using A Drone

This paper 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. Eac...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2024-01, Vol.73 (1), p.1-12
Main Authors: Garcia-Fernandez, Angel F., Xiao, Jimin
Format: Article
Language:English
Subjects:
Algorithms
Cameras
Direction of arrival
drone
Drones
Kalman filters
Mixtures
Monitoring
multiple object tracking
Multiple target tracking
Neural networks
optical and thermal cameras
Optical filters
Optical sensors
Parameter estimation
Traffic monitoring
Trajectory
trajectory Poisson multi-Bernoulli mixture filter
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Summary:This paper 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.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2023.3310742