Toward Privacy-Protecting Safety Systems for Naturalistic Driving Videos
A common pool of naturalistic driving data is necessary to develop and compare algorithms that infer driver behavior, in order to improve driving safety. Naturalistic driving data, such as video sequences of looking at a driver, however, cause concern for the privacy of individual drivers. In an ideal situation, a deidentification filter applied to a raw image of looking at a driver would, semantically, protect the identity and preserve the behavior (e.g., eye gaze, head pose, and hand activity) of the driver. Driver gaze estimation is of particular interest because it is a good indicator of a driver's visual attention and a good predictor of a driver's intent.