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In many parts of the world, the use of vast amounts of data collected onpublic roadways for autonomous driving has increased. In order to detect andanonymize pedestrian faces and nearby car license plates in actual road-drivingscenarios, there is an urgent need for effective solutions. As more data iscollected, privacy concerns regarding it increase, including but not limited topedestrian faces and surrounding vehicle license plates. Normal and fisheyecameras are the two common camera types that are typically mounted oncollection vehicles. With complex camera distortion models, fisheye cameraimages were deformed in contrast to regular images. It causes computer visiontasks to perform poorly when using numerous deep learning models. In this work,we pay particular attention to protecting privacy while yet adhering to severallaws for fisheye camera photos taken by driverless vehicles. First, we suggesta framework for extracting face and plate identification knowledge from severalteacher models. Our second suggestion is to transform both the image and thelabel from a regular image to fisheye-like data using a varied and realisticfisheye transformation. Finally, we run a test using the open-source PP4AVdataset. The experimental findings demonstrated that our model outperformedbaseline methods when trained on data from autonomous vehicles, even when thedata were softly labeled. The implementation code is available at our github:https://github.com/khaclinh/FisheyePP4AV.

FisheyePP4AV: A privacy-preserving method for autonomous vehicles on fisheye camera images