Camera Networks: The Acquisition and Analysis of Videos overWide Areas

133 Pages·2012·2.4 MB·English

by  Amit K. Roy-chowdhury

Camera Networks
TheAcquisitionandAnalysisofVideosoverWideAreas
Synthesis Lectures on Computer
Vision
Editors
GérardMedioni,UniversityofSouthernCalifornia
SvenDickinson,UniversityofToronto
SynthesisLecturesonComputerVisioniseditedbyGérardMedionioftheUniversityof
SouthernCaliforniaandSvenDickinsonoftheUniversityofToronto.Theserieswillpublish50-
to150pagepublicationsontopicspertainingtocomputervisionandpatternrecognition.The
scopewilllargelyfollowthepurviewofpremiercomputerscienceconferences,suchasICCV,
CVPR,andECCV.Potentialtopicsinclude,butnotarelimitedto:
(cid:129) ApplicationsandCaseStudiesforComputerVision
(cid:129) Color,Illumination,andTexture
(cid:129) ComputationalPhotographyandVideo
(cid:129) EarlyandBiologically-inspiredVision
(cid:129) FaceandGestureAnalysis
(cid:129) IlluminationandReflectanceModeling
(cid:129) Image-BasedModeling
(cid:129) ImageandVideoRetrieval
(cid:129) MedicalImageAnalysis
(cid:129) MotionandTracking
(cid:129) ObjectDetection,Recognition,andCategorization
(cid:129) SegmentationandGrouping
(cid:129) Sensors
(cid:129) Shape-from-X
iii
(cid:129) StereoandStructurefromMotion
(cid:129) ShapeRepresentationandMatching
(cid:129) StatisticalMethodsandLearning
(cid:129) PerformanceEvaluation
(cid:129) VideoAnalysisandEventRecognition
CameraNetworks:TheAcquisitionandAnalysisofVideosoverWideAreas
AmitK.Roy-ChowdhuryandBiSong
2011
DeformableSurface3DReconstructionfromMonocularImages
MathieuSalzmannandPascalFua
2010
Boosting-BasedFaceDetectionandAdaptation
ChaZhangandZhengyouZhang
2010
Image-BasedModelingofPlantsandTrees
SingBingKangandLongQuan
2009
Copyright© 2012byMorgan&Claypool
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printedreviews,withoutthepriorpermissionofthepublisher.
CameraNetworks:TheAcquisitionandAnalysisofVideosoverWideAreas
AmitK.Roy-ChowdhuryandBiSong
www.morganclaypool.com
ISBN:9781608456741 paperback
ISBN:9781608456758 ebook
DOI10.2200/S00400ED1V01Y201201COV004
APublicationintheMorgan&ClaypoolPublishersseries
SYNTHESISLECTURESONCOMPUTERVISION
Lecture#4
SeriesEditors:GérardMedioni,UniversityofSouthernCalifornia
SvenDickinson,UniversityofToronto
SeriesISSN
SynthesisLecturesonComputerVision
Print2153-1056 Electronic2153-1064
Camera Networks
TheAcquisitionandAnalysisofVideosoverWideAreas
Amit K.Roy-Chowdhury and Bi Song
UniversityofCalifornia,Riverside
SYNTHESISLECTURESONCOMPUTERVISION#4
M
&C Morgan &cLaypool publishers
ABSTRACT
Asnetworksofvideocamerasareinstalledinmanyapplicationslikesecurityandsurveillance,envi-
ronmentalmonitoring,disasterresponse,andassistedlivingfacilities,amongothers,imageunder-
standingincameranetworksisbecominganimportantareaofresearchandtechnologydevelopment.
Therearemanychallengesthatneedtobeaddressedintheprocess.Someofthemarelistedbelow.
-Traditionalcomputervisionchallengesintrackingandrecognition,robustnesstopose,illumina-
tion,occlusion,clutter,recognitionofobjects,andactivities;
-Aggregatinglocalinformationforwideareasceneunderstanding,likeobtainingstable,long-term
tracksofobjects;
- Positioning of the cameras and dynamic control of pan-tilt-zoom (PTZ) cameras for optimal
sensing;
-Distributedprocessingandsceneanalysisalgorithms;
-Resourceconstraintsimposedbydifferentapplicationslikesecurityandsurveillance,environmental
monitoring,disasterresponse,assistedlivingfacilities,etc.
Inthisbook,wefocusonthebasicresearchproblemsincameranetworks,reviewthecurrent
state-of-the-artandpresentadetaileddescriptionofsomeoftherecentlydevelopedmethodologies.
The major underlying theme in all the work presented is to take a network-centric view whereby
theoveralldecisionsaremadeatthenetworklevel.Thisissometimesachievedbyaccumulatingall
thedataatacentralserver,whileatothertimesbyexchangingdecisionsmadebyindividualcameras
basedontheirlocallysenseddata.
Chapter1startswithanoverviewoftheproblemsincameranetworksandthemajorresearch
directions. Some of the currently available experimental testbeds are also discussed here. One of
thefundamentaltasksintheanalysisofdynamicscenesistotrackobjects.Sincecameranetworks
cover a large area, the systems need to be able to track over such wide areas where there could
bebothoverlappingandnon-overlappingfieldsofviewofthecameras,asaddressedinChapter2.
Distributedprocessingisanotherchallengeincameranetworksandrecentmethodshaveshownhow
todotracking,poseestimationandcalibrationinadistributedenvironment.Consensusalgorithms
thatenablethesetasksaredescribedinChapter3.Chapter4summarizesafewapproachesonobject
andactivityrecognitioninbothdistributedandcentralizedcameranetworkenvironments.Allthese
methods have focused primarily on the analysis side given that images are being obtained by the
cameras.Efficientutilizationofsuchnetworksoftencallsforactivesensing,wherebytheacquisition
and analysis phases are closely linked.We discuss this issue in detail in Chapter 5 and show how
collaborative and opportunistic sensing in a camera network can be achieved. Finally, Chapter 6
concludesthebookbyhighlightingthemajordirectionsforfutureresearch.
KEYWORDS
wideareatracking,distributedvideoanalysis,Kalmanconsensus,distributedtracking,
recognition,activesensing,opportunisticsensing
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Amit:To my parents for all they have done
Bi:To my parents

ix
Contents
Preface .................................................................xiii
1 AnIntroductiontoCameraNetworks........................................1
1.1 Researchdirections ..................................................... 1
1.1.1 CameraNetworkTopology ........................................ 1
1.1.2 WideAreaTracking .............................................. 2
1.1.3 DistributedProcessing ............................................ 3
1.1.4 CameraNetworkControl(ACTIVEvision).......................... 4
1.1.5 MobileCameraNetworks ......................................... 5
1.1.6 SimulationinCameraNetworks .................................... 7
1.1.7 ExperimentalTestbeds ............................................ 7
1.1.8 ApplicationDomains ............................................. 8
1.2 Organizationofthebook ................................................ 9
2 Wide-AreaTracking ..................................................... 11
2.1 ReviewofMulti-TargetTrackingApproaches.............................. 11
2.1.1 KalmanFilter-BasedTracker...................................... 12
2.1.2 ParticleFilter-BasedTracker ...................................... 12
2.1.3 Multi-HypothesisTracking(MHT)................................ 13
2.1.4 JointProbabilisticDataAssociationFilters(JPDAF) ................. 14
2.2 TrackinginaCameraNetwork-ProblemFormulation...................... 14
2.3 AReviewonCameraNetworkTracking .................................. 16
2.4 On-LineLearningUsingAffinityModels................................. 17
2.5 TrackletAssociationUsingStochasticSearch .............................. 19
2.6 PersonReidentification ................................................. 24
2.7 LearningaCameraNetworkTopology ................................... 27
2.8 ConsistentLabelingwithOverlappingFieldsofView ...................... 29
2.9 Conclusions .......................................................... 31
3 DistributedProcessinginCameraNetworks................................ 33
3.1 ConsensusAlgorithmsforDistributedEstimation.......................... 33
x
3.2 DecentralizedandDistributedTracking................................... 35
3.2.1 DecentralizedTracking........................................... 35
3.2.2 DistributedTracking............................................. 36
3.3 ConsensusAlgorithmsforDistributedTracking............................ 36
3.3.1 MathematicalFramework ........................................ 36
3.3.2 ExtendedKalman-ConsensusFilterforaSingleTarget ............... 37
3.3.3 JPDA-EKCFforTrackingMultipleTargets ......................... 40
3.3.4 HandoffinConsensusTrackingAlgorithms......................... 44
3.3.5 ExampleofDistributedTrackingusingEKCF ....................... 44
3.3.6 SparseNetworksandNaiveNodes-TheGeneralizedKalman
ConsensusFilter .................................................48
3.4 CameraNetworkCalibration............................................ 53
3.4.1 DistributedDataAssociation...................................... 53
3.4.2 DistributedCalibration........................................... 54
3.4.3 DistributedPoseEstimation ...................................... 56
3.5 Conclusions .......................................................... 57
4 ObjectandActivityRecognition .......................................... 59
4.1 ObjectRecognition .................................................... 59
4.1.1 ObjectRecognitionUnderResourceConstraints..................... 60
4.2 Time-DelayedCorrelationAnalysis ...................................... 61
4.2.1 SceneDecompositionandActivityRepresentation ................... 62
4.2.2 CrossCanonicalCorrelationAnalysis .............................. 63
4.2.3 Applications .................................................... 63
4.3 ActivityAnalysisUsingTopicModels .................................... 64
4.3.1 ProbabilisticModel .............................................. 65
4.3.2 LabelingTrajectoriesintoActivities ................................ 66
4.4 DistributedActivityRecognition......................................... 66
4.4.1 ConsensusforActivityRecognition ................................ 67
4.5 Conclusions .......................................................... 71
5 ActiveSensing .......................................................... 73
5.1 ProblemFormulation................................................... 73
5.1.1 ActiveSensingofDynamicalProcesses ............................. 74
5.2 ReviewofExistingApproaches .......................................... 77
5.3 CollaborativeSensinginDistributedCameraNetworks ..................... 78
5.3.1 SystemModeling................................................ 79