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

2023/06/29 by García-Fernández, Ángel F., Xiao, Jimin · 1 citation
#Applications (stat.AP) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (stat.ML)

paper · doi:10.48550/arxiv.2306.16890

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.

Cited by

• Integrated Gaussian Processes for Robust and Adaptive Multi-Object Tracking

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