• DocumentCode
    3298360
  • Title

    The Research and Application of Human Detection Based on Support Vector Machine Using in Intelligent Video Surveillance System

  • Author

    Fan, Xinnan ; Xu, Lizhong ; Zhang, Xuewu ; Chen, Lei

  • Author_Institution
    Comput. & Inf. Inst., Hohai Univ., Changzhou
  • Volume
    2
  • fYear
    2008
  • fDate
    18-20 Oct. 2008
  • Firstpage
    139
  • Lastpage
    143
  • Abstract
    This paper presented a special design and implementation of human detection based on SVM (support vector machine) and this method is used in intelligent video surveillance system. In order to simplify the design of the SVM classifier and improve efficiency of machine learning, both a grid vector representation and a center radiating vector representation are proposed to abstract features of the object. The sample data is obtained through processing and analysis including human and no-human which forms the training input to SVM. Finally, we used the trained recognizer to identify whether there is somebody broken into the object region. If there is, the automatic warning device gives the alarm, which guarantees a real-time surveillance.
  • Keywords
    feature extraction; image classification; learning (artificial intelligence); object recognition; support vector machines; video surveillance; SVM classifier; automatic warning device; center radiating vector representation; feature extraction; grid vector representation; human detection; intelligent video surveillance system; machine learning; object recognition; support vector machine; Humans; Intelligent systems; Learning systems; Machine intelligence; Machine learning; Risk management; Statistical learning; Support vector machine classification; Support vector machines; Video surveillance; Grid Vector Representation; Human Detection; SVM; Video Surveillance;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2008. ICNC '08. Fourth International Conference on
  • Conference_Location
    Jinan
  • Print_ISBN
    978-0-7695-3304-9
  • Type

    conf

  • DOI
    10.1109/ICNC.2008.315
  • Filename
    4666973