# License Plate Privacy in Collaborative Visual Analysis of Traffic Scenes

arXiv: https://arxiv.org/abs/2205.01724 · HTML: https://mlsanity.com/paper/2205.01724/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 10 of 10)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services](https://mlsanity.com/paper/1801.08618/) (2018)
- [Scalable Image Coding for Humans and Machines](https://mlsanity.com/paper/2107.08373/) (2021)
- [Deep feature compression for collaborative object detection](https://mlsanity.com/paper/1802.03931/) (2018)
- [Multi-task learning with compressible features for Collaborative Intelligence](https://mlsanity.com/paper/1902.05179/) (2019)
- [Collaborative Intelligence: Challenges and Opportunities](https://mlsanity.com/paper/2102.06841/) (2021)
- [Pareto-Optimal Bit Allocation for Collaborative Intelligence](https://mlsanity.com/paper/2009.12430/) (2020)
- [Bit Allocation for Multi-Task Collaborative Intelligence](https://mlsanity.com/paper/2002.07048/) (2020)

## Built on by (most cited first, 1 of 1)

- [On the Design of Privacy-Aware Cameras: a Study on Deep Neural Networks](https://mlsanity.com/paper/2208.11372/) (2022)

## Abstract (arXiv)

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.