person de-identification in videos
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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Person De-Identification in Videos
Prachi Agrawal and P. J. NarayananIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL.
21, NO. 3, MARCH 2011
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Person De-Identification in Videos
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Outline
• Introduction• De-Identification: General Framework• De-Identification: Proposed Approach– Detect and Track– Segmentation– De-Identification
• Experimental Results• Conclusions
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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.
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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.
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Different Scenarios and De-Identification
• Casual videos• Public surveillance videos• Private surveillance videos
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Subverting De-Identification
• Reversing the de-identification transformation is the most obvious line of attack.
• estimate the blurring function from the de-identified frames
• Randomize is needed.
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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.
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Overview of the method
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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
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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)
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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
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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
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De-Identification
• exponential blur• Weight
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De-Identification
• line integral convolution (LIC)
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Experimental Results
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Experimental Results
• 97.2% and 7.8% hit rates in the case of person detector and face detector
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Experimental Results
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Experimental Results
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Experimental Results
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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