Pedestrian Recognition Using Second-Order HOG Feature View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Hui Cao , Koichiro Yamaguchi , Takashi Naito , Yoshiki Ninomiya

ABSTRACT

Histogram of Oriented Gradients (HOG) is a well-known feature for pedestrian recognition which describes object appearance as local histograms of gradient orientation. However, it is incapable of describing higher-order properties of object appearance. In this paper we present a second-order HOG feature which attempts to capture second-order properties of object appearance by estimating the pairwise relationships among spatially neighbor components of HOG feature. In our preliminary experiments, we found that using harmonic-mean or min function to measure pairwise relationship gives satisfactory results. We demonstrate that the proposed second-order HOG feature can significantly improve the HOG feature on several pedestrian datasets, and it is also competitive to other second-order features including GLAC and CoHOG. More... »

PAGES

628-634

References to SciGraph publications

Book

TITLE

Computer Vision – ACCV 2009

ISBN

978-3-642-12303-0
978-3-642-12304-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-12304-7_59

DOI

http://dx.doi.org/10.1007/978-3-642-12304-7_59

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018418553


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