  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.
