Paper Info
Title
Balancing Privacy and Safety: Protecting Driver Identity in Naturalistic Driving Video Data
Abstract
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.
Year
DOI
Venue
2014
10.1145/2667317.2667325
AutomotiveUI
Keywords
Field
DocType
public policy issues,human factors,driver safety,privacy,de-identification,general
Distraction,Research program,Gaze,De-identification,Simulation,Computer security,Human–computer interaction,Pixel,Personally identifiable information,Engineering,User interface,Automotive industry
Conference
Citations 
PageRank 
References 
0
0.34
14
Authors
3
Name
Order
Citations
PageRank
Sujitha Martin117712.72
Ashish Tawari221916.07
Mohan M. Trivedi36564475.50