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Privacy Protection in Video Surveillance
Challenges and Approaches M. Vijay Venkatesh,
Jian Zhao and Samson Cheung (http//www.vis.uky.ed
u/mialab)Center for Visualization and Virtual
Environments, University of Kentucky , Lexington,
KY-40507

• Object Removal by Inpainting
• Protect privacy of the selected individuals by
  removing them from the video data by Digital
  Inpainting techniques.
• Research Challenges in Digital Inpainting
• Lots of research on texture inpainting but
  Structure Inpainting remains an open problem
• Lack of Subjective and Objective evaluation of
  algorithms
• Video inpainting is slow in execution
• Image Inpainting
• Static objects differing from background are
  removed from the scene by utilizing existing
  image inpainting techniques.

Introduction Video Surveillance has become
ubiquitous and there is a growing concern that it
could severely undermine individuals right to
privacy. The goal of privacy preserving video
surveillance is to explore computational
techniques for modifying video data and
releasing useful information in such a way that
the identity of selected individuals contained in
data cannot be recognized while the data remain
practically useful.

• Data Hiding by Watermarking
• To protect the integrity of the video data being
  modified we need
• preserve the original which can only be accessed
  by proper
• authentication.
• The technical challenges unique to our
  applications are
• Very high payload
• Great concern to maintain perceptual quality
• Combination with encryption and compression.
• The proposed system is given below

• Video Inpainting
• Video inpainting is made faster by
• Utilizing the background model for non-occluding
  cases
• Using a template based instead of patch based
  inpainting model in presence of occlusion
• For non-occluding situations a background
  replacement based
• video inpainting can be utilized.
• Compare pixels with acquired background models
• Create FG by connected component grouping
• Form a bounding box and track based on overlap of
  objects
• To accommodate changes in illumination, construct
  and adaptive BG model
• Replace over the FG and smooth it by a
  de-blocking filter

.
Privacy protected video
DCT
Motion Compensation
Entropy Coding
H.263
H.263
Fig.1. a) Example video b) Modified video in
which the identity of a selected individual is
concealed
Fig. 3. a) Original image b) Image with hole c)
Inpainted image using exampler-based texture
inpainting Criminisi03
Fig. 6. a) Original frame in a video b) Extracted
foreground c) Video inpainting by background
replacement

• Proposed System

Motion inpainting is performed to inpaint the
foreground objects in the presence of occlusions.
Foreground is inpainted in the occlusion region
by using dynamic programming to select the
template (extracted foreground) sequences that
minimize the mean square error before and after
the occlusion.
Fig.9. Data hiding model in DCT domain
Private Object Selection

• Issues with watermarking
• About eight-fold increase in bit-rate (QP10).
  Why?
• Compression efficiency achieved by
• Lots of empty residue blocks
• Non-zero DCT coefficients occur in groups
• Spread-out watermark bits violate this model.
• Solution
• Rank each block (rather than each coefficient)
  based on their sensitivity
• Restrict the embedding to the least sensitive few
  blocks

Encryption
Video Compression
Object Detection and Tracking
Fig. 4. Top-left Image with hole Top-right
Segmented Image (based on K-means) Bottom-left
Inferred boundary based on cubic spline
Bottom-right texture in-painting inside and
outside the boundary
Object Removal
Data Hiding Compression
Background Construction
Reduced-Reference Perceptual Inpainting
Evaluation
Compressed Original 119 kbps
Texture and Shape attributes within and outside
the hole region are compared by computing the
chi-square statistic of the distribution of high
Frequency Angular Radial Transform (ART)
coefficients and distribution of curvature.
Fig. 7. Extracted foreground images before and
after the occlusions
Compressed Foreground 50 kbps
Fig.2. Proposed system for privacy protection in
video surveillance
The results of our motion inpainting are shown in
the following figure.

• Challenges
• We identify the two main challenges that are
  unique to this system
• Object removal by Inpainting
• Hiding private information as a invisible
  watermark

Fig. 5) Segmented mask of inpainted regions
Compressed Watermarked w/ foreground 628
kbps (x3.7 times)
Compressed Modified 119 kbps
Fig.10. a) Original Video b) Extracted Foreground
c) Video with foreground removed d) Video with
foreground Watermarked
Fig. 8. a) Original frame in a video b) With hole
c) Video inpainting by template based dynamic
programming technique.
Table 1. Chi-Square statistic comparison of
distribution features