Multi-sensor Multi-target Tracking Using Labelled Random Finite Sets with Homography Data
Jonah Ong, Du Yong Kim, Sven Erik Nordholm · 2019
This paper proposes a solution for multi-sensor multi-target tracking with homography data using the labelled random finite set with a top-down Bayesian recursion formulation. The proposed method encapsulates multi-target state motion, appearance and disappearance and all aspects of noise, detection and association uncertainty from multiple sensors. This technique naturally incorporates the fusion of multi-sensor measurements to improve the fidelity of multi-target trajectories estimation. A linear Gaussian multi-target model with simulated homography data from multiple sensors is undertaken for verification.