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

• URL: http://arxiv.org/abs/2306.16890v2
• Date: Mon, 28 Aug 2023 19:38:32 GMT
• Title: Trajectory Poisson multi-Bernoulli mixture filter for traffic monitoring using a drone
• Authors: \'Angel F. Garc\'ia-Fern\'andez and Jimin Xiao
• Abstract 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. We have tested the accuracy of the resulting TPMBM filter in synthetic and experimental data sets.
• Score: 17.636403357588897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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