Object detection VIA boosted deformable features

Authors:
Mohamed Hussein;Fatih Porikli;Larry Davis
Affiliations:
University of Maryland and Mitsubishi Electric Research Labs, Cambridge, MA;Mitsubishi Electric Research Labs, Cambridge, MA;University of Maryland, Dept. of Computer Science, College Park, MD
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

It is a common practice to model an object for detection tasks as a boosted ensemble of many models built on features of the object. In this context, features are defined as subregions with fixed relative locations and extents with respect to the object's image window. We introduce using deformable features with boosted ensembles. A deformable features adapts its location depending on the visual evidence in order to match the corresponding physical feature. Therefore, deformable features can better handle deformable objects. We empirically show that boosted ensembles of deformable features perform significantly better than boosted ensembles of fixed features for human detection.