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Multi-view People Detection on Arbitrary Ground in Real-Time

Multi-view People Detection on Arbitrary Ground in Real-Time. Ákos Kiss, Tamás Szirányi Distributed Events Analysis Research Laboratory kiss.akos@sztaki.mta.hu. Multi-View People Detection Overview. Problem definition Methods Our approach Utilizing height map Results Summary.

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Multi-view People Detection on Arbitrary Ground in Real-Time

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  1. Multi-view People Detection on Arbitrary Ground in Real-Time Ákos Kiss, Tamás Szirányi DistributedEventsAnalysis Research Laboratory kiss.akos@sztaki.mta.hu

  2. Multi-ViewPeopleDetection Overview • Problemdefinition • Methods • Ourapproach • Utilizingheightmap • Results • Summary

  3. Multi-ViewPeopleDetection Problemdefinition • Multiplecameras monitoring an area • Overlappingfield of view • Additionalspatialinformation • Pixelscorrespondtolinesinspace • Linesintersectwhereobjectlies • Triangulation • Pixelsfromforegroundmask • Manynumber of line pairs

  4. Multi-ViewPeopleDetection Methods • Intensiveresearchinthisfield • Foregroundmask projected togroundplane • Shadowsappear • Probabilisticoptimisations

  5. Multi-ViewPeopleDetection • Methods • Intensiveresearchinthisfield • Projection toreferenceview • Synergy map • Rectifiedtoground

  6. Multi-ViewPeopleDetection Methods • Homographiesconnectplanes • Flatground is assumed • Occupancyatdifferentheights (projection to parallel planes) • Fewexamples of differentmethods • Epipolargeometry (discardspatialinformation) • Occupancy of boundingboxes (spatialposition, height map has to be known)

  7. Multi-ViewPeopleDetection Ourapproach • Basedonintersecting 3D primitives • Conesinstead of lines • Advantages of ourapproach: • Resultsin 3D position • No flatground is required • Number of conesdoesn’tgrowwithresolution • Real-timeoperation is possible • Onthedownside • Fullycalibratedcameras (intrinsic/extrinsicparameters) • Precise Time synchronization

  8. Multi-ViewPeopleDetection Ourapproach • Formingprimitives • Findingintersections • Detectingobjects

  9. Multi-ViewPeopleDetection Ourapproach Verticallines foot: bottom • Formingprimitives • Filteringcandidatepixels • Clusteringcandidatepixels • Modelingwithellipse • Back-projecting tocone • Findingintersections • Detectingobjects

  10. Multi-ViewPeopleDetection Ourapproach • Formingprimitives • Filteringcandidatepixels • Clusteringcandidatepixels • Modelingwithellipse • Back-projecting tocone • Findingintersections • Detectingobjects Unrotatedellipse: Rotatedellipse:

  11. Multi-ViewPeopleDetection Ourapproach • Formingprimitives • Filteringcandidatepixels • Clusteringcandidatepixels • Modelingwithellipse • Back-projecting tocone • Findingintersections • Detectingobjects

  12. Multi-ViewPeopleDetection Ourapproach • Ellipticalcones • Conesaremodeledwithorthogonalvectors • Bevelanglescodedinvectorlengths • Formingprimitives • Filteringcandidatepixels • Clusteringcandidatepixels • Modelingwithellipse • Back-projecting tocone • Findingintersections • Detectingobjects

  13. Multi-ViewPeopleDetection Ourapproach • Ellipsescoveringfeetaresmall • Smallbevelangles • Intersectionshould be nearclosestpoints of axes • Formingprimitives • Findingintersections • Coneintersection is complex • Approximation • Intersection of cylinders • Linearproblem • Detectingobjects

  14. Multi-ViewPeopleDetection Ourapproach • Ellipticalcylinder • Goal is tomaximizeinnersphere • Pointonbothaxes • Practicallyneverintersect • Linearoptimization • considering major and minor axes • p is theintersectionpoint (match) • Formingprimitives • Findingintersections • Coneintersection is complex • Approximation • Intersection of cylinders • Linearproblem • Detectingobjects

  15. Multi-ViewPeopleDetection Ourapproach • Formingprimitives • Findingintersections • Detectingobjects • Detection is denseregions of matches • Weight is applied (error of linearsystem – distancefromaxes) • Objectposition is baricenter of matches

  16. Multi-View People Detection • Utilizingheightmap • Detector output is noisy • Highnumber of false positives • Height Map • False positives are mostly far from ground • Height map can be reconstructedusingstatisticalfiltering • Precision is increased drastically with filtering to height map ROC curveswith (red) and without (blue) filteringtoheight map

  17. Multi-ViewPeopleDetection • Results • Facing a number of possibleerrorsources • Foregroundartifacts • Accidentalmatches • Time synchronization

  18. Multi-ViewPeopleDetection • Results • Facing a number of possibleerrorsources • Foregroundartifacts • Accidentalmatches • Time synchronization

  19. Multi-ViewPeopleDetection • Results • Facing a number of possibleerrorsources • Foregroundartifacts • Accidentalmatches • Time synchronization

  20. Multi-ViewPeopleDetection • Results • Height map is reconstructedwithsmallerror • Meanerror is less than 2cm • People (either leg) detection is quitereliable • Real-timeprocessing(17fps) • Foregrounddetectiontook 86-91% of time • Parallelizable, distributable blue: EPFL terrace (plane) green: SZTAKI (table and boxes) Height of floor

  21. Multi-View People Detection • Summary • Weaddressedthecase of non planarground • Ourmethod is capable of determiningfeetpositions • Ouralgorithm is capable of real-timerunning • Distributedcomputation is alsopossible

  22. ThankYou!

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