Fast recognition method of pedestrian signs based on colour features and SVM

被引:0
|
作者
Gan, Lu [1 ]
机构
[1] Hunan City Univ, Art & Design Coll, Yiyang, Hunan, Peoples R China
关键词
colour characteristics; SVM; sidewalk sign; HSV colour model; median filtering;
D O I
10.1504/IJCAT.2024.143291
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In order to overcome the problems of low recognition efficiency and poor recognition accuracy in traditional recognition methods of pedestrian signs, a fast recognition method of pedestrian signs based on colour features and SVM is proposed. Firstly, the median filter is used to filter the salt and pepper noise in the image, and the RGB image of the sidewalk indicator is converted into HSV image. Secondly, two morphological processing methods, expansion and corrosion, are used to obtain the colour features of the sidewalk indicator image. Finally, SVM is used to classify the image of sidewalk indicator signs, construct the loss function of sidewalk indicator sign image recognition and solve it to get the final image recognition result. The experimental results show that the recognition algorithm in this paper can achieve 98.2% recognition accuracy, and the recognition time is always less than 5 s.
引用
收藏
页数:10
相关论文
共 50 条
  • [21] An improved method for the character recognition based on SVM
    Hu, CW
    Zhao, YN
    Wang, JX
    Yang, ZH
    PROCEEDINGS OF THE IASTED INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND APPLICATIONS, 2006, : 457 - +
  • [22] An iris recognition method based on multi-orientation features and Non-symmetrical SVM
    Gu Hong-ying
    Zhuang Yue-ting
    Pan Yun-he
    Journal of Zhejiang University-SCIENCE A, 2005, 6 (5): : 428 - 432
  • [23] FIRE: Fast Iris REcognition on mobile phones by combining colour and texture features
    Galdi, Chiara
    Dugelay, Jean-Luc
    PATTERN RECOGNITION LETTERS, 2017, 91 : 44 - 51
  • [24] A Fast Handwritten Digit Recognition Algorithm Based on Improved SVM
    Li, Qiong
    Chen, Li
    PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON INFORMATION SCIENCES, MACHINERY, MATERIALS AND ENERGY (ICISMME 2015), 2015, 126 : 2093 - 2096
  • [25] Pedestrian Detection Based on Bag-of-Visual-Words and SVM method
    Li, Jun
    Liao, Yuanjiang
    Zhang, Hongmei
    ADVANCES IN MECHATRONICS AND CONTROL ENGINEERING III, 2014, 678 : 189 - 192
  • [26] Handwritten Digit Recognition based on DCT features and SVM Classifier
    El Qacimy, Bouchra
    Kerroum, Mounir Ait
    Hammouch, Ahmed
    2014 SECOND WORLD CONFERENCE ON COMPLEX SYSTEMS (WCCS), 2014, : 13 - 16
  • [27] Object Recognition Using SVM Based Bag of Combined Features
    Mehboob, Fozia
    Abbas, Muhammad
    Rauf, Abdul
    INTELLIGENT COMPUTING, VOL 1, 2019, 858 : 497 - 510
  • [28] Human Action Recognition Based on SVM Using Multiple Features
    Huang, Xianping
    Zheng, Lili
    Liang, Ronhua
    Wang, Wanliang
    Ma, Xiangyin
    2012 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING (ICAISC 2012), 2012, 12 : 160 - 165
  • [29] Aircraft type recognition based on convex hull features and SVM
    Liu, Yuan
    Wu, Xiuqin
    Hong, Richang
    MIPPR 2007: AUTOMATIC TARGET RECOGNITION AND IMAGE ANALYSIS; AND MULTISPECTRAL IMAGE ACQUISITION, PTS 1 AND 2, 2007, 6786
  • [30] Study of Objectionable Sound Recognition based on Histogram Features and SVM
    Shi, ZiQiang
    Gao, BoYang
    Han, JiQing
    Wu, Zhen
    PROCEEDINGS OF THE 2009 2ND INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, VOLS 1-9, 2009, : 4505 - +