Non-Redundant Gradient Semantic Local Binary Patterns for pedestrian detection

Jiu Xu, Ning Jiang, Satoshi Goto

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

In this paper, a feature named Non-Redundant Gradient Semantic Local Binary Patterns (NRGSLBP) is proposed for pedestrian detection as a modified version of conventional Semantic Local Binary Patterns (SLBP). Calculations of this feature are carried out for both intensity and gradient magnitude image so that texture and gradient information are combined. Moreover, non-redundant patterns are adopted on SLBP for the first time, allowing better discrimination. Compared with SLBP, no additional cost of the feature dimensions NRGSLBP is necessary and the calculation complexity is considerably smaller than that of other features. Experimental results on several datasets show that the detection rate of our proposed feature outperforms those of other features such as Histogram of Orientated Gradient (HOG), Histogram of Templates (HOT), Bidirectional Local Template Patterns (BLTP), Gradient Local Binary Patterns (GLBP), SLBP and Covariance matrix (COV).

本文言語English
ホスト出版物のタイトルEuropean Signal Processing Conference
出版社European Signal Processing Conference, EUSIPCO
ページ1407-1411
ページ数5
ISBN(印刷版)9780992862619
出版ステータスPublished - 2014 11 10
イベント22nd European Signal Processing Conference, EUSIPCO 2014 - Lisbon
継続期間: 2014 9 12014 9 5

Other

Other22nd European Signal Processing Conference, EUSIPCO 2014
CityLisbon
Period14/9/114/9/5

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学

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