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Balancing Privacy and Safety: Protecting Driver Identity in Naturalistic Driving Video Data

Naturalistic driving dataset is at the heart of automotive user interface research, detecting/measuring driver distraction, and many other driver safety related studies. Recent advances in the collection of large scale naturalistic driving data include the second Strategic Highway Research Program (SHRP2) consisting of more than 3000 subjects and the 100-Car study. Public access to such data, however, is made difficult due to personal identifiable information and protection of privacy. We propose de-identification filters for protecting the privacy of drivers while preserving sufficient details to infer driver behavior, such as the gaze direction, in naturalistic driving videos. Driver's gaze estimation is of particular interest because it is a good indicator of driver's visual attention and a good predictor of driver's intent. We implement and compare de-identification filters, which are made up of a combination of preserving eye regions, superimposing head pose encoded face mask and replacing background with black pixels, and show promising results.