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

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 de-identified 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