Multiple Object Trajectory Estimation Using Backward Simulation
Journal article, 2022
This paper presents a general solution for computing the multi-object posterior for sets of trajectories from a sequence of multi-object (unlabelled) filtering densities and a multi-object dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multi-object filters that do not explicitly estimate trajectories. In this paper, we first derive a general multi-trajectory backward smoothing equation based on random finite sets of trajectories. Then we show how to sample sets of trajectories using backward simulation for Poisson multi-Bernoulli filtering densities, and develop a tractable implementation based on ranked assignment. The performance of the resulting multi-trajectory particle smoothers is evaluated in a simulation study, and the results demonstrate that they have superior performance in comparison to several state-of-the-art multi-object filters and smoothers.
backward simulation
Trajectory
Density measurement
Mathematical models
Electrical engineering
random finite sets
Probabilistic logic
Multi-object tracking
forward-backward smoothing
sets of trajectories
Smoothing methods
Signal processing algorithms
Author
Yuxuan Xia
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Lennart Svensson
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Angel F. Garcia-Fernandez
University of Liverpool
Jason L. Williams
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Daniel Svensson
NVIDIA
Karl Granström
Embark Trucks Inc.
IEEE Transactions on Signal Processing
1053-587X (ISSN) 1941-0476 (eISSN)
Vol. 70 3249-3263Subject Categories
Probability Theory and Statistics
Signal Processing
Computer Vision and Robotics (Autonomous Systems)
DOI
10.1109/TSP.2022.3184794