Approximate Initialization of Camera Sensor Networks Purushottam Kulkarni K.R. School of Information Technology Indian Institute of Technology, Bombay Deepak Ganesan, Prashant Shenoy Department of Computer Science University of Massachusetts, Amherst
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Approximate Initialization of Camera Sensor Networks
Approximate Initialization of Camera Sensor Networks. Purushottam Kulkarni K.R. School of Information Technology Indian Institute of Technology, Bombay. Deepak Ganesan, Prashant Shenoy Department of Computer Science University of Massachusetts, Amherst. Field-of -view. - PowerPoint PPT Presentation
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Approximate Initialization of Camera Sensor Networks
Purushottam KulkarniK.R. School of Information TechnologyIndian Institute of Technology, Bombay
Deepak Ganesan, Prashant ShenoyDepartment of Computer Science
University of Massachusetts, Amherst
UNIVERSITY OF MASSACHUSETTS, AMHERST 2
Camera Sensor Networks
Wireless network of tetherless imaging sensors◊ Directional camera sensors
Applications◊ Ad-hoc Surveillance◊ Environmental and habitat monitoring
Tasks◊ Object detection, recognition, tracking
Field-of-view
UNIVERSITY OF MASSACHUSETTS, AMHERST 3
Camera Initialization
Pre-requisite for applications tasks◊ Localization, requires camera coordinates◊ Duty-cycling, requires set/overlap of neighbors◊ Tracking, requires overlap location with neighbors
k-overlap: ratio of randomly placed reference objects viewed simultaneously by k cameras
cameras take pictures determine if object can be viewed simultaneously by
other cameras
Camera 3
Camera 2
Camera 1
kk ii
i
rO
r
reference points viewed at camera iir
kirreference points viewed by k cameras
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Skewed Distributions
Fraction of points does not represent fraction of overlap◊ Points in sparse region actually represent larger region◊ Error in estimation due to non-uniform distribution
Camera 3
Camera 2
Camera 111O
21O31O
: 2/3
: 1/9
: 2/9
11O
21O31O
: 1/2
: 1/4
: 1/4
Estimated Exact
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Handling Skewed Distributions
Assign area of each polygon as weight to corresponding reference point◊ Weight in proportion to density of neighbors
kk ii
i
wO
w
Total weight of reference points viewed at camera iiw
kiw Total weight of reference points viewed by k cameras
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Approximate 3D Voronoi Tessellation
Accurate 3D tessellation◊ Compute intensive
Approximation◊ Discretize volume into cubes◊ Calculate closest reference point
◊ Add volume to closest◊ Points in spare regions will have higher weights
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Determining Region of Overlap
where the overlap exists between cameras
region of overlap is the union of cells containing all simultaneously visible points
C1 C2
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Estimate dr using object size, image size, focal length
& have same orientation
Use unit vector along and dr to estimate location
Estimating Reference Point Location
f
rd
s
s’
Lens
P(-x,-y,-f)O
Rrd
rv (unknownlocation)Image
plane'
r
s stan
d f
CCCCCCCCCCCCCCPO
CCCCCCCCCCCCCCrv
CCCCCCCCCCCCCCPO
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Outline
Introduction & Problem Statement
Approximate Initialization Parameters
Estimation Techniques
Experimental Evaluation
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Experimental Evaluation
Simulation◊ 150 x 150 x 150◊ Two scenarios
◊ 4 cameras◊ 12 cameras
◊ Non-uniform distribution◊ Fraction of objects restricted area
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Experimental Evaluation
Implementation◊ 8 Cyclops camera sensors◊ Crossbow Micaz nodes◊ 8ft x 6ft x 17ft
Image GrabberObject Detection
Bounding Box
CyclopsView Table
Initializationprocedure
HostMotetrigger
viewinformation
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Weighted Approximation
Demonstrates non-weighted scheme shortcoming◊ Performs 4-6 times worse than weighted
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Effect of Skew
Weighted scheme can correct for skew better◊ Non-weighted scheme worse by a factor of 6
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Region of overlap
Error decreases with #reference points◊ ~22% with 12 pts/camera◊ 10% with 37 pts/camera