Deep Learning for Model-Based Multiobject Tracking
Journal article, 2023

Multiobject tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements, with important applications, such as autonomous driving, tracking animal behavior, defense systems, and others. The MOT task can be divided into two settings, model based or model free, depending on whether accurate and tractable models of the environment are available. Model-based MOT has Bayes-optimal closed-form solutions, which can achieve state-of-the-art (SOTA) performance. However, these methods require approximations in challenging scenarios to remain tractable, which impairs their performance. Deep learning (DL) methods offer a promising alternative, but existing DL models are almost exclusively designed for a model-free setting and are not easily translated to the model-based setting. This article proposes a DL-based tracker specifically tailored to the model-based MOT setting and provides a thorough comparison to SOTA alternatives. We show that our DL-based tracker is able to match performance to the benchmarks in simple tracking tasks while outperforming the alternatives as the tasks become more challenging. These findings provide strong evidence of the applicability of DL also to the model-based setting, which we hope will foster further research in this direction.

multiobject tracking (MOT)

Deep learning (DL)

random finite sets (RFS)

uncertainty prediction

Transformers

Author

Juliano Pinto

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Georg Hess

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

William Ljungbergh

Linköping University

Yuxuan Xia

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Lennart Svensson

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

IEEE Transactions on Aerospace and Electronic Systems

0018-9251 (ISSN) 15579603 (eISSN)

Vol. 59 6 7363-7379

Subject Categories

Other Computer and Information Science

Control Engineering

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/TAES.2023.3289164

More information

Latest update

7/5/2024 7