1 / 22

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

carney
Download Presentation

Person De-Identification in Videos

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Person De-Identification in Videos PrachiAgrawal and P. J. Narayanan IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 21, NO. 3, MARCH 2011

  2. Person De-Identification in Videos

  3. Outline • Introduction • De-Identification: General Framework • De-Identification: Proposed Approach • Detect and Track • Segmentation • De-Identification • Experimental Results • Conclusions

  4. 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.

  5. 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.

  6. Different Scenarios and De-Identification • Casual videos • Public surveillance videos • Private surveillance videos

  7. 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.

  8. 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.

  9. Overview of the method

  10. 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

  11. 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)

  12. 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

  13. Segmentation • The representative color vn for a voxelshould 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

  14. De-Identification • exponential blur • Weight

  15. De-Identification • line integral convolution (LIC)

  16. Experimental Results

  17. Experimental Results • 97.2% and 7.8% hit rates in the case of person detector and face detector

  18. Experimental Results

  19. Experimental Results

  20. Experimental Results

  21. 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

More Related