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Vehicle detection algorithm based on codebook and local binary patterns algorithms
  • ISSN号:1671-1610
  • 期刊名称:《现代大学教育》
  • 时间:0
  • 分类:TP391.4[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术] U492.22[交通运输工程—交通运输规划与管理;交通运输工程—道路与铁道工程]
  • 作者机构:[1]School of Physics and Electronics, Central South University,Changsha 410083,China
  • 相关基金:Project(61172047)supported by the National Natural Science Foundation of China
中文摘要:

Detecting the moving vehicles in jittering traffic scenes is a very difficult problem because of the complex environment.Only by the color features of the pixel or only by the texture features of image cannot establish a suitable background model for the moving vehicles. In order to solve this problem, the Gaussian pyramid layered algorithm is proposed, combining with the advantages of the Codebook algorithm and the Local binary patterns(LBP) algorithm. Firstly, the image pyramid is established to eliminate the noises generated by the camera shake. Then, codebook model and LBP model are constructed on the low-resolution level and the high-resolution level of Gaussian pyramid, respectively. At last, the final test results are obtained through a set of operations according to the spatial relations of pixels. The experimental results show that this algorithm can not only eliminate the noises effectively, but also save the calculating time with high detection sensitivity and high detection accuracy.

英文摘要:

Detecting the moving vehicles in jittering traffic scenes is a very difficult problem because of the complex environment.Only by the color features of the pixel or only by the texture features of image cannot establish a suitable background model for the moving vehicles. In order to solve this problem, the Gaussian pyramid layered algorithm is proposed, combining with the advantages of the Codebook algorithm and the Local binary patterns(LBP) algorithm. Firstly, the image pyramid is established to eliminate the noises generated by the camera shake. Then, codebook model and LBP model are constructed on the low-resolution level and the high-resolution level of Gaussian pyramid, respectively. At last, the final test results are obtained through a set of operations according to the spatial relations of pixels. The experimental results show that this algorithm can not only eliminate the noises effectively, but also save the calculating time with high detection sensitivity and high detection accuracy.

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期刊信息
  • 《现代大学教育》
  • 北大核心期刊(2011版)
  • 主管单位:中华人民共和国教育部
  • 主办单位:中南大学 湖南省高等教育学会
  • 主编:胡岳华
  • 地址:湖南长沙岳麓区中南大学高教所<现代大学教育>编辑部
  • 邮编:410083
  • 邮箱:MUEbjb@mail.csu.edu.cn
  • 电话:0731-88876856
  • 国际标准刊号:ISSN:1671-1610
  • 国内统一刊号:ISSN:43-1358/G4
  • 邮发代号:42-173
  • 获奖情况:
  • 首届湖南文科学报质量评比一等奖,湖南省一级刊物
  • 国内外数据库收录:
  • 中国北大核心期刊(2004版),中国北大核心期刊(2008版),中国北大核心期刊(2011版),中国北大核心期刊(2014版),中国国家哲学社会科学学术期刊数据库
  • 被引量:13521