Approximate Initialization of Camera Sensor Networks

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


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


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


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


Outline

Introduction & Problem Statement

Approximate Initialization Parameters

Estimation Techniques

Experimental Evaluation


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



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


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


Triggered Wakeup

Wakeup scenarios◊ Object tracking◊ Reliable detection

Region of overlap can determine potential cameras

C1

C2 C3

Object


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


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


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


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


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


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


Outline

Introduction & Problem Statement

Approximate Initialization Parameters

Estimation Techniques




Simulation◊ 150 x 150 x 150◊ Two scenarios

◊ 4 cameras◊ 12 cameras

◊ Non-uniform distribution◊ Fraction of objects restricted area



Implementation◊ 8 Cyclops camera sensors◊ Crossbow Micaz nodes◊ 8ft x 6ft x 17ft

Image GrabberObject Detection

Bounding Box

CyclopsView Table

Initializationprocedure

HostMotetrigger

viewinformation


Weighted Approximation

Demonstrates non-weighted scheme shortcoming◊ Performs 4-6 times worse than weighted


Effect of Skew

Weighted scheme can correct for skew better◊ Non-weighted scheme worse by a factor of 6


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


Applications

Duty-cycling◊ Weighted scheme outperforms non-weighted

Triggered wakeup◊ 80% positive wakeups with 10 pts/camera with 2 triggers

Duty-Cycling Triggered Wakeup


Implementation Results

k-overlap estimation error: 2-9%

Region of overlap error: 1-11%

Approximate techniques feasible in real deployments (~10% error)


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]


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


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



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


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

Documents

Approximate Initialization of Camera Sensor Networks