Extended Object Tracking Using Sets of Trajectories with a PHD Filter
Paper in proceeding, 2021

PHD filtering is a common and effective multiple object tracking (MOT) algorithm used in scenarios where the number of objects and their states are unknown. In scenarios where each object can generate multiple measurements per scan, some PHD filters can estimate the extent of the objects as well as their kinematic properties. Most of these approaches are, however, not able to inherently estimate trajectories and rely on ad-hoc methods, such as different labeling schemes, to build trajectories from the state estimates. This paper presents a Gamma Gaussian inverse Wishart mixture PHD filter that can directly estimate sets of trajectories of extended targets by expanding previous research on tracking sets of trajectories for point source objects to handle extended objects. The new filter is compared to an existing extended PHD filter that uses a labeling scheme to build trajectories, and it is shown that the new filter can estimate object trajectories more reliably.

PHD filtering

Multiple object tracking

Bayesian smoothing

Random finite sets

Extended objects

Gamma Gaussian inverse Wishart

Trajectories

Author

Jakob Sjudin

SafeRadar Research Sweden

Martin Marcusson

SafeRadar Research Sweden

Lennart Svensson

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Lars Hammarstrand

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Proceedings of 2021 IEEE 24th International Conference on Information Fusion, FUSION 2021

961-968
9781737749714 (ISBN)

24th IEEE International Conference on Information Fusion, FUSION 2021
Sun City, South Africa,

Subject Categories

Robotics

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

More information

Latest update

11/22/2022