物体識別のための低計算コスト画像特徴Sparse FINDの提案

概要

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In this paper, a novel image feature named Sparse FIND which is suitable for onboard pedestrian sensor due to having low computational cost is introduced. Histograms of Oriented Gradients (HOG) have been shown excellent capability of image classification. Feature correlation descriptor (FIND) which has achieved higher capability of image classification in comparison with HOG has been developed. However, FIND requires much more computational cost than HOG due to computing the correlation between histogram elements. In Sparse FIND, the computational cost is reduced by limiting the computation of the correlations between histogram elements which contribute classification majorly. Through experiments, Sparse FIND performs nearly the same capability of image classification as FIND does despite the computational cost is approximately one tenth in comparison with it of FIND, is confirmed.