5 research outputs found
Two-layer particle filter for multiple target detection and tracking
This paper deals with the detection and tracking of an unknown number of targets using a Bayesian hierarchical model with target labels. To approximate the posterior probability density function, we develop a two-layer particle filter. One deals with track initiation, and the other with track maintenance. In addition, the parallel partition method is proposed to sample the states of the surviving targets
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. 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