Multiple target tracking based on sets of trajectories

Abstract

We propose a solution of the multiple target tracking (MTT) problem based on sets of trajectories and the random finite set framework. A full Bayesian approach to MTT should characterise the distribution of the trajectories given the measurements, as it contains all information about the trajectories. We attain this by considering multi-object density functions in which objects are trajectories. For the standard tracking models, we also describe a conjugate family of multitrajectory density functions.

MATLAB implementations of algorithms based on sets of trajectories can be found at https://github.com/Agarciafernandez

References in corpus (5)

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Cited by in corpus (16)

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• Continuous-discrete multiple target tracking with out-of-sequence measurements
• Tracking multiple spawning targets using Poisson multi-Bernoulli mixtures on sets of tree trajectories
• Poisson Multi-Bernoulli Mixtures for Sets of Trajectories
• Multiple Object Trajectory Estimation Using Backward Simulation
• Extended target Poisson multi-Bernoulli mixture trackers based on sets of trajectories
• From Target Tracking to Targeting Track -- Part I: A Metric for Spatio-Temporal Trajectory Evaluation
• Poisson Multi-Bernoulli Approximations for Multiple Extended Object Filtering
• From Target Tracking to Targeting Track: A Data-Driven Yet Analytical Approach to Joint Target Detection and Tracking
• Backward Simulation for Sets of Trajectories
• Poisson multi-Bernoulli mixture filter for trajectory measurements