• DocumentCode
    1566401
  • Title

    Automatic vehicle classification instrument based on multiple sensor information fusion

  • Author

    Liu, Weiming ; Zhao, Xueping ; Xiao, Jingfang ; Wu, Youlong

  • Author_Institution
    Sch. of Traffic & Transp. Eng., Central South Univ., Changsha, China
  • Volume
    1
  • fYear
    2005
  • Firstpage
    379
  • Abstract
    This paper presents a kind of automatic vehicle classification (AVC) instrument for expressway toll collection system based on multiple sensor information fusion technique according to the complicated characteristics of vehicle types in China, In order to develop the instrument, we make use of the video detection segregator, infrared detection technique, piezomagnetic sensor, multiple sensor information fusion technique based on BP neural network. The training result is obtained by 1500 training samples and the training accuracy rate is up to 99.4%. And the simulation accuracy rate on 500 samples is up to 99.2%. The results show that the classification precision is high and the instrument has great value to be popularized.
  • Keywords
    image classification; neural nets; object detection; road vehicles; sensor fusion; video signal processing; AVC instrument; BP neural network; China vehicle type; automatic vehicle classification; classification precision; expressway toll collection system; infrared detection; multiple sensor information fusion; piezomagnetic sensor; simulation accuracy rate; training accuracy rate; training sample; video detection segregator; Automatic voltage control; Axles; Infrared detectors; Infrared sensors; Instruments; Sensor fusion; Sensor phenomena and characterization; Thin film sensors; Vehicles; Wheels; AVC; ETC; Multiple Sensor Information Fusion; Neural Network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Technology and Applications, 2005. ICITA 2005. Third International Conference on
  • Print_ISBN
    0-7695-2316-1
  • Type

    conf

  • DOI
    10.1109/ICITA.2005.82
  • Filename
    1488831