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

License Plate Privacy in Collaborative Visual Analysis of Traffic Scenes