Transcript Person De-Identification in Videos

Person De-Identification in
Videos
Prachi Agrawal and P. J. Narayanan
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL.
21, NO. 3, MARCH 2011
Person De-Identification in Videos
Outline
• Introduction
• De-Identification: General Framework
• De-Identification: Proposed Approach
– Detect and Track
– Segmentation
– De-Identification
• Experimental Results
• Conclusions
Introduction
• This has raised new concerns regarding the
privacy of individuals.
• Videos over the internet invaded our privacy.
• Some technologies like Google street view, or
surveillance video.
• It is needed to person de-identification in
videos.
Introduction
• Face recognition and human detection are
accurate recently.
• But just black out the face or human will lose
many information in the video.
• In this paper, they use two blur methods to
remain more information.
Different Scenarios and
De-Identification
• Casual videos
• Public surveillance videos
• Private surveillance videos
Subverting De-Identification
• Reversing the de-identification transformation
is the most obvious line of attack.
• estimate the blurring function from the deidentified frames
• Randomize is needed.
Storage of Videos
• The safest approach is to de-identify the video
at the capture-camera.
• Some situation need the original video.
• Final approach is to store the original video,
with sufficiently hard encryption, along with
the de-identified video.
Overview of the method
Detect and Track
• HOG based human detector.
• patch-based recognition approach for object
tracking by voting.
• apply the human detector every F frames.
• set F to 40 for our experiments
Segmentation
• Multiple video tubes are formed if there are
multiple people in the video.
• voxels of size (x × y × t) in the spatial (x, y) and
temporal (t) domains. (4*4*2)
Segmentation
•
•
U is the data term and V1, V2 are the smoothness terms corresponding to the
intra-frame and inter-frame
The Gaussian mixture models (GMMs) are used for adequately modeling data
points in the color space
Segmentation
• The representative color vn for a voxel should
be chosen carefully.
• distance D0 and D1 to the background and
foreground
• The pixels are sorted on the ratio D0/ D1 in
the decreasing order.
• D1 is low in mth pixel, seed foreground
• D0 is low in (N-m)th pixel , seed background
De-Identification
• exponential blur
• Weight
De-Identification
• line integral convolution (LIC)
Experimental Results
Experimental Results
• 97.2% and 7.8% hit rates in the case of person
detector and face detector
Experimental Results
Experimental Results
Experimental Results
Conclusion
• presented a basic system to protect privacy
against algorithmic and human recognition
• We also conducted a user study to evaluate
the effectiveness of our system.
• characteristics are difficult to hide if familiarity
is high to the user