DMask: A Reliable Identity Masking System for Driver Safety Video Data
项目名称:
DMask: A Reliable Identity Masking System for Driver Safety Video Data
摘要:
The research consists of developing a four-layered processing framework to detect and track facial features (if possible); to detect the head in the remaining frames; to replace the head with an avatar in all frames; and to evaluate the confidence of the identity masking. A graphical user interface will be developed that a person can use to verify success. The implementation of the four-layered processing consists of nine tasks: (1) collect samples of videos from the second Strategic Highway Research Program (SHRP2) 24-car (SHRP2-24) study dataset and retrain the face detector; (2) track and extract facial features from the SHRP2-24 data by looking at relative movements of tracked facial points; (3) track and extract face and head pose from the SHRP2-24 data using the random sample consensus (RANSAC) method and a three-dimensional (3D) face model; (4) develop and apply the capability to track eye gaze without an infrared (IR) camera and with low-resolution video, which will be developed and applied to the SHRP2-24 dataset using an adaptive appearance-based eye gaze estimation method; (5) develop a method to interpolate head position in frames that the tracker missed using dense-trajectory-based interpolation methods and apply it to the SHRP2-24 data; (6) develop the capability to synthesize facial motion on a computer-generated avatar; (7) develop the capability to render avatars over the videos at the appropriate head location for identity masking; (8) develop a method to detect (not mask) nonfacial elements that obscure the face using fine-grained alpha-mask extraction; and (9) develop a graphical user interface (GUI) tool to obtain confidence of masking over the entire video, which will also allow a user to quickly view the lowest confidence frames in the masked video.