Fast HOG based person detection devoted to a mobile robot with a spherical camera
Résumé
In this paper, we present a fast Histogram of Oriented Gradients (HOG) based person detector. The detector adopts a cascade of rejectors framework by selecting discrimi-nant features via a new proposed feature selection framework based on Binary Integer Programming. The mathematical programming explicitly formulates an optimization problem to select discriminant features taking detection performance and computation time into account. The learning of the cascade classifier and its detection capability are validated using a proprietary dataset acquired using the Ladybug2 spherical camera and the public INRIA person detection dataset. The final detector achieves a comparable detection performance as Dalal and Triggs [2] detector while achieving on average more than 8x speed up on image from the Ladybug2 .
Origine | Fichiers produits par l'(les) auteur(s) |
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