  Traffic scene analysis is important for emerging technologies such as smart
traffic management and autonomous vehicles. However, such analysis also poses
potential privacy threats. For example, a system that can recognize license
plates may construct patterns of behavior of the corresponding vehicles' owners
and use that for various illegal purposes. In this paper we present a system
that enables traffic scene analysis while at the same time preserving license
plate privacy. The system is based on a multi-task model whose latent space is
selectively compressed depending on the amount of information the specific
features carry about analysis tasks and private information. Effectiveness of
the proposed method is illustrated by experiments on the Cityscapes dataset,
for which we also provide license plate annotations.
