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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
Initialization parameters:◊ Extrinsic: location, orientation
◊ Intrinsic: focal length, skew, principal point
◊ Set of neighbors
◊ Degree of overlap
UNIVERSITY OF MASSACHUSETTS, AMHERST 4
Factors Effecting Initialization
Computation Capability Infrastructure Support
◊ Range Estimation◊ Landmarks
sync
range estimationpulse
Cricket Mote
Camera Sensor Networks Landmarks hard to find Resource-constraints
Estimation of accurate parameters not possible
UNIVERSITY OF MASSACHUSETTS, AMHERST 5
Problem Statement
Given a CSN with,◊ Limited computation capability◊ No/minimal infrastructure support
is it possible to initialize cameras to enable applications?
Proposed solution: Approximate Initialization◊ Estimate relative relationships between cameras◊ Use only picture taking capability and local
processing of camera
UNIVERSITY OF MASSACHUSETTS, AMHERST 6
Outline
Introduction & Problem Statement
Approximate Initialization Parameters
Estimation Techniques
Experimental Evaluation
UNIVERSITY OF MASSACHUSETTS, AMHERST 7
Approximate Initialization
Degree of Overlap◊ Fraction of viewing region that overlaps with
neighboring cameras
◊ k-overlap: fraction of viewing region overlapping by k cameras
Approximates level of sensing redundancy with neighboring cameras
UNIVERSITY OF MASSACHUSETTS, AMHERST 8
Approximate Initialization
Region of Overlap◊ spatial volume within viewing region that
overlaps with another camera
◊ Degree of overlap does not estimate which portion overlaps with neighbors
Approximates location of neighbors and spatial region of overlap
Approximate estimates can support application requirements
UNIVERSITY OF MASSACHUSETTS, AMHERST 9
Duty-Cycling
Operate in ON-OFF cycles d:duty-cycling parameter (ON fraction)
Oik: k-overlap of camera
Parameter in proportion to degree of overlap (extent of redundant coverage)
1
1nk
i ik
d ok
UNIVERSITY OF MASSACHUSETTS, AMHERST 10
Triggered Wakeup
Wakeup scenarios◊ Object tracking◊ Reliable detection
Region of overlap can determine potential cameras
C1
C2 C3
Object
UNIVERSITY OF MASSACHUSETTS, AMHERST 11
Estimating k-overlap
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
UNIVERSITY OF MASSACHUSETTS, AMHERST 12
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
UNIVERSITY OF MASSACHUSETTS, AMHERST 13
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
UNIVERSITY OF MASSACHUSETTS, AMHERST 14
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
UNIVERSITY OF MASSACHUSETTS, AMHERST 15
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
UNIVERSITY OF MASSACHUSETTS, AMHERST 16
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
UNIVERSITY OF MASSACHUSETTS, AMHERST 17
Outline
Introduction & Problem Statement
Approximate Initialization Parameters
Estimation Techniques
Experimental Evaluation
UNIVERSITY OF MASSACHUSETTS, AMHERST 18
Experimental Evaluation
Simulation◊ 150 x 150 x 150◊ Two scenarios
◊ 4 cameras◊ 12 cameras
◊ Non-uniform distribution◊ Fraction of objects restricted area
UNIVERSITY OF MASSACHUSETTS, AMHERST 19
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
UNIVERSITY OF MASSACHUSETTS, AMHERST 20
Weighted Approximation
Demonstrates non-weighted scheme shortcoming◊ Performs 4-6 times worse than weighted
UNIVERSITY OF MASSACHUSETTS, AMHERST 21
Effect of Skew
Weighted scheme can correct for skew better◊ Non-weighted scheme worse by a factor of 6
UNIVERSITY OF MASSACHUSETTS, AMHERST 22
Region of overlap
Error decreases with #reference points◊ ~22% with 12 pts/camera◊ 10% with 37 pts/camera
Error ~10% in region of overlap estimation
UNIVERSITY OF MASSACHUSETTS, AMHERST 23
Applications
Duty-cycling◊ Weighted scheme outperforms non-weighted
Triggered wakeup◊ 80% positive wakeups with 10 pts/camera with 2 triggers
Duty-Cycling Triggered Wakeup
UNIVERSITY OF MASSACHUSETTS, AMHERST 24
Implementation Results
k-overlap estimation error: 2-9%
Region of overlap error: 1-11%
Approximate techniques feasible in real deployments (~10% error)
UNIVERSITY OF MASSACHUSETTS, AMHERST 25
Related Work
Camera calibration◊ Accurate Extrinsic and Intrinsic parameters [Tsai 86], [Tsai
87], [Zhang 00]
Multimedia Sensor Networks◊ Panoptes: A vision sensor [Feng 03]
◊ Audio sensors [Raykar 03]
Localization◊ Sensor Localization [He 03], [Savvides 01], [Whitehouse 02]
◊ Active Badge [Harter 94], RADAR [Bahl 00], Cricket [Priyantha 00], Active Bat [Ward 97], GPS
◊ Relative Locationing [Rao 03]
UNIVERSITY OF MASSACHUSETTS, AMHERST 26
Conclusions
Proposed approximate techniques to estimate associations between cameras◊ Degree and region of overlap
Demonstrated use of estimates to enable applications◊ Error in estimations tolerable
http://sensors.cs.umass.edu
UNIVERSITY OF MASSACHUSETTS, AMHERST 27
Technology Trends
Sensors/platforms span a large spectrum Enable heterogeneous camera networks
Stargate
Funct
ion
alit
y
Cyclops
CMUcam
Webcam
Mote
Telos
XYZ
Image Sensors Sensor platforms
PTZ
Energy
Funct
ion
alit
y
Energy
UNIVERSITY OF MASSACHUSETTS, AMHERST 28
Approximate Initialization
Degree of overlap◊ Extent of overlapping coverage◊ k-overlap: fraction of viewing area covered by k cameras
Region of overlap◊ where is the overlapping coverage◊ spatial region of overlap with neighboring
cameras
Above estimates can support application requirements
UNIVERSITY OF MASSACHUSETTS, AMHERST 29
Triggered Wakeup
Wakeup scenarios◊ Object tracking◊ Reliable detection
Determine best camera◊ Projection line
◊ Object along this line◊ Reference points within
distance threshold◊ Extent of overlap
determines best camera
Image
Projectionline
Object
Distancethreshold
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