  Algorithmic solutions for multi-object tracking (MOT) are a key enabler for
applications in autonomous navigation and applied ocean sciences.
State-of-the-art MOT methods fully rely on a statistical model and typically
use preprocessed sensor data as measurements. In particular, measurements are
produced by a detector that extracts potential object locations from the raw
sensor data collected for a discrete time step. This preparatory processing
step reduces data flow and computational complexity but may result in a loss of
information. State-of-the-art Bayesian MOT methods that are based on belief
propagation (BP) systematically exploit graph structures of the statistical
model to reduce computational complexity and improve scalability. However, as a
fully model-based approach, BP can only provide suboptimal estimates when there
is a mismatch between the statistical model and the true data-generating
process. Existing BP-based MOT methods can further only make use of
preprocessed measurements. In this paper, we introduce a variant of BP that
combines model-based with data-driven MOT. The proposed neural enhanced belief
propagation (NEBP) method complements the statistical model of BP by
information learned from raw sensor data. This approach conjectures that the
learned information can reduce model mismatch and thus improve data association
and false alarm rejection. Our NEBP method improves tracking performance
compared to model-based methods. At the same time, it inherits the advantages
of BP-based MOT, i.e., it scales only quadratically in the number of objects,
and it can thus generate and maintain a large number of object tracks. We
evaluate the performance of our NEBP approach for MOT on the nuScenes
autonomous driving dataset and demonstrate that it has state-of-the-art
performance.
