Evaluation of Manually Created Ground Truth for Multi-view People Localization

1. Evaluation of ManuallyCreatedGroundTruthforMulti-viewPeopleLocalization Ákos Kiss, Tamás Szirányi DistributedEventsAnalysis Research Laboratory kiss.akos@sztaki.mta.hu Sziranyi.tamas@sztaki.mta.hu

2. Multi-ViewPeopleLocalization Overview • Problemdefinition • Motivation • Ourframework • Contribution • Referencegroundtruth • Evaluating human observers • Summary

3. Multi-ViewPeopleLocalization Problemdefinition • Multiplecameras monitoring an area • Overlappingfield of view • Additionalspatialinformation • Pixelscorrespondtolinesinspace • Linesintersectwhereobjectlies • Triangulation • Groundtruthrequired

4. Multi-ViewPeopleLocalization Problemdefinition • Creatinggroundtruth • Manually • Positioninimages • Boundingboxes • Positioninreferencespace • Parametricsurface • True 3D positions • Automatically • ToFsensors • Kinect • Lidar - Expensivesolutions - Occlusion is still a problem

5. Multi-ViewPeopleLocalization Motivation • Groundtruthusuallytakenforgranted • Human makemistakes • Poorgroundtruthleadstoinvalidalgorithmevaluation • Generatinggroundtruth is timeconsuming • Experts’ time is expensive • Evaluating human observers • 9 subjects (6 laymen, 3 withdomainknowledge)

6. Multi-ViewPeopleLocalization Ourframework • GUI – allviewsvisible • Locationbytriangulation • Mark inany (atleast 2) views • Triangulation • Feedback (savelocationonlyifcorrect) • Localizingpersonbyperson • Head • Feet • Skipfootifnotvisiblein 2 views

7. Multi-ViewPeopleLocalization Ourframework • Localizingbylinearoptimization • p is onbothlines ( axis): • Linespracticallyneverintersect • Pseudoinverse (minimalizes SSE) • Validatingwithknownsurface • Planarground is reconstructedprecisely

8. Multi-ViewPeopleLocalization Referencegroundtruth • Several „groundtruth” createdbysubjects • Combininginformationfrommultiplegroundtruth • Peoplearelocalizedbyonly a subset of subjects • Positionsarenoisy • Automatingprocess • Match peoplebyhead (body) location • Matchingfeet (ordermightdiffer) • Filteringnoise (weighted center of locations) • More reliableresult: referencegroundtruth

9. Multi-ViewPeopleLocalization Evaluating human observers • Errors (precision) • Accuracy (locationerror) • Recall (missedpeople) • Temporalanalysis

10. Multi-ViewPeopleLocalization Evaluating human observers • Errors (precision) • Lowerrorrate • Typicalerrors • Parallel lines (mark near camera) • Mix uppeople • Accuracy (locationerror) • Recall (missedpeople) • Temporalanalysis

11. Multi-ViewPeopleLocalization Evaluating human observers • Errors (precision) • Accuracy (locationerror) • Synchronizationerror • Lowdeviation • Outliersuppression • Iterative • Changeweight of points • Recall (missedpeople) • Temporalanalysis

12. Multi-ViewPeopleLocalization Evaluating human observers • Errors (precision) • Accuracy (locationerror) • Recall (missedpeople) • Verylowrecall • Expertsarenotbetter • Temporalanalysis feet = headrecall line recallvalues of laymen (blue) and experts (green)

13. Multi-ViewPeopleLocalization Evaluating human observers • Errors (precision) • Accuracy (locationerror) • Recall (missedpeople) • Temporalanalysis • Shortexperiment • Fewsubjects • Less outliersforexperts

14. Multi-ViewPeopleLocalization Summary • Typical human observer • Highprecision • Highaccuracy • Lowrecall • Generatingthistype of groundtruthrequiresmuchattention • Generating more reliablereferencegroundtruthcan be automated

15. ThankYou!