  Multitarget Tracking (MTT) is the problem of tracking the states of an
unknown number of objects using noisy measurements, with important applications
to autonomous driving, surveillance, robotics, and others. In the model-based
Bayesian setting, there are conjugate priors that enable us to express the
multi-object posterior in closed form, which could theoretically provide
Bayes-optimal estimates. However, the posterior involves a super-exponential
growth of the number of hypotheses over time, forcing state-of-the-art methods
to resort to approximations for remaining tractable, which can impact their
performance in complex scenarios. Model-free methods based on deep-learning
provide an attractive alternative, as they can, in principle, learn the optimal
filter from data, but to the best of our knowledge were never compared to
current state-of-the-art Bayesian filters, specially not in contexts where
accurate models are available. In this paper, we propose a high-performing
deep-learning method for MTT based on the Transformer architecture and compare
it to two state-of-the-art Bayesian filters, in a setting where we assume the
correct model is provided. Although this gives an edge to the model-based
filters, it also allows us to generate unlimited training data. We show that
the proposed model outperforms state-of-the-art Bayesian filters in complex
scenarios, while matching their performance in simpler cases, which validates
the applicability of deep-learning also in the model-based regime. The code for
all our implementations is made available at
https://github.com/JulianoLagana/MT3 .
