  Vision is one of the primary sensing modalities in autonomous driving. In
this paper we look at the problem of estimating the velocity of road vehicles
from a camera mounted on a moving car. Contrary to prior methods that train
end-to-end deep networks that estimate the vehicles' velocity from the video
pixels, we propose a two-step approach where first an off-the-shelf tracker is
used to extract vehicle bounding boxes and then a small neural network is used
to regress the vehicle velocity from the tracked bounding boxes. Surprisingly,
we find that this still achieves state-of-the-art estimation performance with
the significant benefit of separating perception from dynamics estimation via a
clean, interpretable and verifiable interface which allows us distill the
statistics which are crucial for velocity estimation. We show that the latter
can be used to easily generate synthetic training data in the space of bounding
boxes and use this to improve the performance of our method further.
