Deep Learning for Model-Based Multiobject Tracking
Artikel i vetenskaplig tidskrift, 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



Juliano Pinto

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Georg Hess

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

William Ljungbergh

Linköpings universitet

Yuxuan Xia

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Henk Wymeersch

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Lennart Svensson

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

IEEE Transactions on Aerospace and Electronic Systems

0018-9251 (ISSN) 15579603 (eISSN)

Vol. 59 6 7363-7379


Annan data- och informationsvetenskap


Datorseende och robotik (autonoma system)



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