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Histograms of Oriented Gradients for Human Detection - Inria

HistogramsofOrientedGradientsforHumanDet ectionNavneetDalalandBillTriggsINRIARh studythequestionoffeature setsforrobustvisualob-jectrecognition,ad optinglinearSVMbasedhumandetec-tionasa testcase. Afterreviewingexistingedge andgra-dientbaseddescriptors, weshowexperimentallythatgridsofHistogram sofOrientedGradient(HOG)descriptors sig-ni cantlyoutperformexistingfeature studythein uenceofeach stage ofthecomputationonperformance, concludingthat ne-scalegradients, neorientationbinning, relativelycoarsespatialbinning, andhigh-qualitylocalcontrastnormalizatio ninoverlappingde-scriptorblocksare givesnear-perfectseparationontheoriginal MITpedestriandatabase, soweintroducea more challengingdatasetcontainingover1800anno tatedhumanimageswitha large range a challengingtaskowingtotheirvariableappea ranceandthewiderangeofposesthatthey rstneedisa robustfeaturesetthatallowsthehumanformto bediscriminatedcleanly, eveninclutteredbackgroundsunderdif studytheissueoffeaturesetsforhumandetect ion,showingthatlo-callynormalizedHistogr amofOrientedGradient(HOG)

dient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors sig-nicantly outperform existing feature sets for human detec-tion. We study the inuence of each stage of the computation on performance, concluding that ne-scale gradients, ne orientation binning, relatively coarse spatial binning, and

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