PP4AV: A benchmarking Dataset for Privacy-preserving Autonomous Driving

Abstract

Massive data collected on public roads for autonomous driving has become more popular in many locations in the world. More collected data leads to more concerns about data privacy, including but not limited to pedestrian faces and surrounding vehicle license plates, which urges for robust solutions for detecting and anonymizing them in realistic road-driving scenarios. Existing public datasets for both face and license plate detection are either not focused on autonomous driving or only in parking lots. In this paper, we introduce a challenging public dataset for face and license plate detection in autonomous driving domain. The dataset is aggregated from visual data that is available in public domain, to cover scenarios from six European cities, including daytime and nighttime, annotated with both faces and license plates. All of the images feature a variety of poses and sizes for both faces and license plates. Our dataset offers not only a benchmark for evaluating data anonymization models but also data to get more insights about privacy-preserving autonomous driving. The experimental results showed that 1) current generic state-of-the-art face and/or license plate detection models do not perform well on a realistic and diverse road- driving dataset like ours, 2) our model trained with autonomous driving data (even with soft-labeling data) out- performed strong but generic models, and 3) the size of faces and license plates is an important factor for evaluating and optimizing the performance of privacy-preserving autonomous driving. The annotation of dataset as well as baseline model and results are available at our github: https://github.com/khaclinh/pp4av.