Pedestrian recognition using second-order HOG feature

Authors:
Hui Cao;Koichiro Yamaguchi;Takashi Naito;Yoshiki Ninomiya
Affiliations:
Road Environment Recognition Lab, Toyota Central R&D LABS.,INC., Nagakute, Aichi, Japan;Road Environment Recognition Lab, Toyota Central R&D LABS.,INC., Nagakute, Aichi, Japan;Road Environment Recognition Lab, Toyota Central R&D LABS.,INC., Nagakute, Aichi, Japan;Road Environment Recognition Lab, Toyota Central R&D LABS.,INC., Nagakute, Aichi, Japan
Venue:
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Year:
2009

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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.