  The majority of contemporary object-tracking approaches do not model
interactions between objects. This contrasts with the fact that objects' paths
are not independent: a cyclist might abruptly deviate from a previously planned
trajectory in order to avoid colliding with a car. Building upon HART, a neural
class-agnostic single-object tracker, we introduce a multi-object tracking
method MOHART capable of relational reasoning. Importantly, the entire system,
including the understanding of interactions and relations between objects, is
class-agnostic and learned simultaneously in an end-to-end fashion. We explore
a number of relational reasoning architectures and show that
permutation-invariant models outperform non-permutation-invariant alternatives.
We also find that architectures using a single permutation invariant operation
like DeepSets, despite, in theory, being universal function approximators, are
nonetheless outperformed by a more complex architecture based on multi-headed
attention. The latter better accounts for complex physical interactions in a
challenging toy experiment. Further, we find that modelling interactions leads
to consistent performance gains in tracking as well as future trajectory
prediction on three real-world datasets (MOTChallenge, UA-DETRAC, and Stanford
Drone dataset), particularly in the presence of ego-motion, occlusions, crowded
scenes, and faulty sensor inputs.
