FisheyePP4AV: A privacy-preserving method for autonomous vehicles on
  fisheye camera images
Linh Trinh Bach Ha Tu Tran
In many parts of the world, the use of vast amounts of data collected on
public roadways for autonomous driving has increased. In order to detect and
anonymize pedestrian faces and nearby car license plates in actual road-driving
scenarios, there is an urgent need for effective solutions. As more data is
collected, privacy concerns regarding it increase, including but not limited to
pedestrian faces and surrounding vehicle license plates. Normal and fisheye
cameras are the two common camera types that are typically mounted on
collection vehicles. With complex camera distortion models, fisheye camera
images were deformed in contrast to regular images. It causes computer vision
tasks to perform poorly when using numerous deep learning models. In this work,
we pay particular attention to protecting privacy while yet adhering to several
laws for fisheye camera photos taken by driverless vehicles. First, we suggest
a framework for extracting face and plate identification knowledge from several
teacher models. Our second suggestion is to transform both the image and the
label from a regular image to fisheye-like data using a varied and realistic
fisheye transformation. Finally, we run a test using the open-source PP4AV
dataset. The experimental findings demonstrated that our model outperformed
baseline methods when trained on data from autonomous vehicles, even when the
data were softly labeled. The implementation code is available at our github:
https://github.com/khaclinh/FisheyePP4AV.