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VolzFestschrift-Talk-Song

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CollaborativeObservationofNaturalEnvironments(CONE)DezhenSongTexasA&MUniversityApr.10th,2010,WorkshoponIntelligentSystems:AFestschriftforRichardVolz,TAMU,CollegeStation,TXThanksto:NiQin,YiliangXu,ChangYoungKim,JiZhangTAMUKenGoldberg,UCBerkeleyRonRohrbach,CornellLabofOrnithologyJohnFitzpatrick,CornellLabofOrnithologyDavidLuneau,UArkansasJohnRappole,SmithsonianSelmaGlasscock,WelderWildlifeFoundationNationalScienceFoundationTheNatureConservancyArkansasGameandFishCommissionU.S.FishandWildlifeServiceArkansasElectricCooperativeCacheRiverNationalWildlifeRefugeoutline• networkedtelerobotsandprojectcone• automatedobservation:ivorybilledwoodpecker• engagingcitizenscientists:birdrangechangeinsouthtexasnikolatesla(1898)teleoperation:relatedwork• Tesla,1898• Goertz,ʻ54• Mosher,ʻ64• Tomovic,ʻ69• Salisbury,Bejczy,ʻ85• Ballard,ʼ86• Volz,ʼ87-• Sheridan,ʻ92• Sato,ʼ94• Goldberg,ʼ94-• PresenceJournalʻ92-• O.Khatib,etal.ʼ96collaborativecontrolnetworkedtelerobotsnetworkedroboticcamera12FrameSelectionProblem:Givennrequests,findoptimalframeProcessingZoomTypeComplexityCentralizedDiscreteExactO(n2)CentralizedDiscreteApproxO(nklog(nk)),k=(log(1/ε)/ε)2CentralizedContin.ExactO(n3)CentralizedContin.ApproxO((n+1/ε3)log2n) DistributedDiscreteExactServer:O(n),Client:O(n)DistributedContin.ApproxServer:O(n),ClientO(1/ε3)p-Frame DiscreteApproxO(n/ε3+p2/ε6)frameselectionalgorithmsbiologicalobservationisarduous,expensive,dangerous,lonelyMotionSensorsPeriodicChecksSensorNetworkStudentsBiologistsCollaborativeframeselection:coneoutline• networkedtelerobotsandprojectcone• automatedobservation:ivorybilledwoodpecker• engagingcitizenscientists:birdrangechangeinsouthtexasDetecting Rare Birds • Low occurrence (e.g., <10 times per year) • Short duration (e.g., < 1 sec. in FOV) • Huge video data for human identification. • Setup and maintenance in remote environments.Design Goals • Accuracy – low false negative • Data reduction – filtering the targeted bird • Easy to setup and maintain – monocular vision system25• Crittercam • DeerCam • Africa web cams at the Te m b e Elephant part • Tiger web cams • James Reserve Wildlife Observatory • Crane Cam • Swan Cam NaturalcamerasRelated Work • Motion detection and tracking – Elgammal, Grimson, Isard … • Periodic motion detection – Culter, Ran, Briassouli … • 3D inference using monocular vision – Ribnick, Hoiem, Saxena …Related Work • Kalman Filter – SLAM, tracking, recognition … – Convergence • ample observation data • manageable noise • less than 11 data points • significant image noiseBird detection problem • Input – targeted bird body length lb and speed range V=[vmin,vmax]. – a sequence of n images containing a moving object • Output – to determine if the object is a bird of targeted speciesAssumptions • Static monocular camera – High resolution – Narrow FOV • Single bird in FOV – Motion segmentation • Constant bird velocity – High flying speed – Narrow camera FOVConjecture 1: Invariant body lengthConjecture 1: Invariant body length [ut,vt]T [uh,vh]T l z=[uh,vh,ut,vt]T (observation)Bird Body Axis Filtering • Conjecture 2: Body axis orientation close to tangent line of trajectory Bird body axis Flying trajectory θ θ Difference between θ and θ on 61bird sequences: BModeling A Flying Bird camera center Image plane [x,y,z]T [ut,vt]T [uh,vh]T x!z!y!lb Ptail Dynamics: Pin-hole model: Tail: lExtended Kalman Filter camera center Image plane x(k+1) z(k) z(k+1) x!z!y!lb x(k) lDetermine Species for Noise-free Cases camera center Image plane Targeted!range!False TrueEKF Convergence MetricsEstimation with Observation Noises camera center Image planeProbable Observation Data Set (PODS) camera center Image plane PODS: Targeted!range!PODS-EKF PODS: Targeted!range!Dezhen Song and Yiliang Yu, A Low False Negative Filter for Detecting Rare Bird Species from Short Video Segments using a Probable Observation Data Set-based EKF Method, IEEE Transactions on Image Processing (Accepted, in press)PODS-EKF Approximate Computation Targeted!range!Dezhen Song and Yiliang Yu, A Low False Negative Filter for Detecting Rare Bird Species from Short Video Segments using a Probable Observation Data Set-based EKF Method, IEEE Transactions on Image Processing (Accepted, in press) Subject to:PODS-EKF Approximate Computation Dezhen Song and Yiliang Yu, A Low False Negative Filter for Detecting Rare Bird Species from Short Video Segments using a Probable Observation Data Set-based EKF Method, IEEE Transactions on Image Processing (Accepted, in press)AlgorithmExperiments: • Testing phase: May 2006 to Oct. 2006 in Texas A&M campus • Field phase: Oct. 2006 to Oct. 2007 in Brinkley, AR Experiments and ResultsSimulation on three birdsPhysical Experiment on Rock Pigeon Insects, falling leaves, other birds, etc.ROC


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