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Tutorial: multicamera and distributed video surveillance

Tutorial: multicamera and distributed video surveillance. Third ACM/IEEE International Conference on Distributed Smart Cameras ICDSC 2009 30/08/2009     Como (Italy). Prof. Rita Cucchiara Università di Modena e Reggio Emilia, Italy. Distributed surveillance.

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Tutorial: multicamera and distributed video surveillance

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  1. Tutorial: multicamera and distributed video surveillance Third ACM/IEEE International Conference on Distributed Smart Cameras ICDSC 2009 30/08/2009     Como (Italy) Prof. Rita Cucchiara Università di Modena e Reggio Emilia, Italy

  2. Distributedsurveillance • Problemoftracking and distributedconsistentlabeling : Problemofmatching or recognizingobjectspreviouslyviewedbyothercameras. • Some constraints: • Constraints on the motionmodels and transitiontimes • Scene planarityforbothoverlapping and notoverlappingFOVs • Constraintsofrecurrentpaths [70] V. Kettenker, R. ZabihBayesian multi camera surveillance CVPR 1999 [54] C.Stauffer, K.Tieu automated multi-camera planar tracking correspondence modeling cvpr 2003

  3. Distributedsurveillance (cont.) • Network of (smart) cameras; NotoverlappedFoVs; looselycoupled. • Problemsofnodecommunication • Ifmovingcameras: problemsofcalibration and tracking. The simultaneous localization and tracking (SLAT) problem, to estimate both the trajectory of the object and the poses of the cameras. • Problem of color calibration original Independentchannels Full matrix Look-uptable [71]ZoltanSafar, John Aa. Sørensen, JianjunChen, and K°are J. Kristoffersen MULTIMODAL WIRELESS NETWORKS: DISTRIBUTED SURVEILLANCE WITH MULTIPLE NODES ProcofICASSP 2005 [72]Funiak, S.; Guestrin, C.; Paskin, M.; Sukthankar, R.; Distributed localization of networked cameras Int conf on Information Processing in Sensor Networks, 2006. 

  4. Color calibration • Methods: • Linear transformation • Independentchannels • Full matrix ( M conmputedwith LSQ) • Look-uptable • for non linear • transformation [73]Roullot, E., "A unifying framework for color image calibration," 15th International Conference on Systems, Signals and Image Processing, 2008. IWSSIP 2008, pp.97-100, 25-28 June 2008 [74]K. Yamamoto and J. U “Color Calibration for Multi-Camera System by using Color Pattern Board” Technical Report MECSE-3-2006

  5. Featureto match • Color (single / multiple) • Shape (geometricalratios / spline / ellipticalmodels) • Motion (speed, direction) • Gait (Fourier transform) • SIFT + , grey level co-occurrence matrix, Zernike moments and some simple colour features • Polar color histogram + Shape [75]Nicholas J. Redding, Julius Fabian Ohmer1, Judd Kelly1 & TristromCookeCross-Matching via Feature Matching for Camera Handover with Non-OverlappingFieldsofView Proc. Of DICTA2008 [76]Kang, Jinman; Cohen, Isaac; Medioni, Gerard, "PersistentObjectsTrackingAcross Multiple Non OverlappingCameras," IEEE Workshop on Motion and Video Computing, 2005. WACV/MOTIONS '05, vol.2, no., pp.112-119, Jan. 2005

  6. DistributedSurveillance at ImageLab • The problem: a people disappeared in the scene exitingfrom a camera FoV, where can bedetected in the future? • 1) trackingwithin a camera FoVmulti hypothesis generation • 2) tracking in exitzones • 3) Predictionintonewcameras’ FoVs • 4) matching in the enteringzones • Using Particle Filtering + Pathnodes • In computer graphic all the possible avatar positions are represented by nodes and the connecting arcs refers to allowed paths. The sequence of visited nodes is called pathnodes. A weight can be associated to each arc in order to give some measures on it, such as the duration, the likelihood to be chosen with respect to other paths, and so on. • Weights can bedefined or learned in a testingphase [77]R. Vezzani, D. Baltieri, R. Cucchiara, "PathnodesintegrationofstandaloneParticleFiltersfor people tracking on distributedsurveillancesystems" in Proceedingsof 25° ICIAP2009, 2009

  7. Exploit the knowledgeabout the scene • Toavoidall-to-allmatches, the tracking system can exploit the knowledgeabout the scene • Preferentialpaths -> Pathnodes • Borderline / exitzones • Physicalconstraints & Forbiddenzones NVR • Temporalconstraints

  8. Trackingwithpathnode A possiblepathbetweenCamera1 and Camera 4

  9. Pathnodesleadparticlediffusion

  10. Resultswith PF and pathnodes Single camera tracking:Multicamera tracking Recall=90.27% Recall=84.16% Precision=88.64% Precision=80.00%

  11. Example Frame 431. a man #21 exits and hisparticles are propagated Frame 452 a person # 22 exitstoo and alsohisparticles are propagated Frame 471 a people isdetected in Camera #2 and the particlesofboth # 21 and #22 are usedbut the onesof #22 match and person 22 isrecognized

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