Trajectory Poisson multi-Bernoulli mixture filter for traffic monitoring using a drone
This work addresses traffic monitoring for urban or surveillance applications, but it is incremental as it applies an existing Bayesian MOT algorithm to a new sensor setup.
The paper tackled multi-object tracking for traffic monitoring using a drone with optical and thermal cameras, proposing a trajectory Poisson multi-Bernoulli mixture filter that achieved accurate vehicle trajectory estimation in synthetic and experimental datasets.
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.