Respectful Cameras
Jeremy Schiff
EECS Department
University of California, Berkeley
Ken Goldberg, Marci Meingast,
Deirdre Mulligan, Pam Samuelson
IEOR, EECS, Law
University of California, Berkeley
http://www.cs.berkeley.edu/~jschiff/RespectfulCameras
NSF Science and Technology Center, Team for Research in
Ubiquitous Secure Technologies, NSF CCF-0424422, with
additional support from Cisco, HP, IBM, Intel, Microsoft,
Symmantec, Telecom Italia and United Technologies.
Background
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New class of Robotic Cameras since 9/11/2001
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$20,000 -> Under $1,000
Static -> Pan, tilt, zoom (21x)
UK - 3 Million Outdoor Cameras
Now Deploying in Large US Cities
Zoom Example
Invasiveness
Objective
Static Marker Detection
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Adaboost
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Training Phase
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Classifying Phase
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Input is data and label
Data -> label
Linear function of
weak classifiers
Example
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Construction Hat
Color
Features
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Input from images
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Each pixel
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red, green, blue (RGB)
Values 0 to 255
Project into higher dimension
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Convert to 9 dimensions
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RGB
HSV
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Stable over changing lighting
LAB
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Good for detecting specularities
10
243
13
9
241
16
12
252
8
60
201
73
69
225
74
42
17
38
65
209
78
74
220
171
45
112
16
Classifiers
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Operates on each dimension
Threshold value
Above good and below bad
 Above bad and below good
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Example
Connected Component
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Groups adjacent pixels
Threshold
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Minimum Area
Bounding Box
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Acceptable Ratio Between Dimensions
Marker Tracking
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Particle Filtering
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Probabilistic Method
for Tracking
Motivates Probabilistic
AdaBoost
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Particle filters
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Non-Parametric
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Sample Based Method (Particles)
Particle Density ~ Likelihood
Tracking requires three distributions
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Initialization Distribution
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Transition Model (Intruder Model)
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Observation Model
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Determines
Observation Model
1-p
p
0.1
0.1
0.1
0.2
0.0
0.8
0.6
0.4
0.2
0.7
0.9
0.4
0.3
0.2
0.1
0.2
0.9
0.9
0.9
0.8
1.0
0.8
0.6
0.6
0.8
0.7
0.9
0.6
0.7
0.8
0.9
0.8
0.79375
Transition Model
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State
Position
 Bounding-box Width
 Bounding-box Height
 Orientation
 Speed
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Add Gaussian Noise to width,
height, orientation and speed
Euler Integration to determine
new position
Multiple Filters
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Single Filter Per Marker
Define overlap
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Add Filter when overlap of Static Image Cluster
and all filters is below threshold
Delete Filter when prob. of best particle < 0.5
Delete Filter when 2 filters overlap > threshold
Video – Nearby Hats
Video – Nearby Hats
Video – Lighting
Video – Lighting
Video – Crossing
Video – Crossing
Video – Shirt
Video – Shirt
Future Work
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Other Features
Edge Detection
 Feature Structure
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Generalize to Other Domains
Other Obstruction Mechanisms
Encryption
 Full Body
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Multiple Cameras
Thank You
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Jeremy Schiff: jschiff@cs.berkeley.edu
URL: www.cs.berkeley.edu/~jschiff/RespectfulCameras