• DocumentCode
    1251951
  • Title

    Estimation of number of people in crowded scenes using perspective transformation

  • Author

    Lin, Sheng-Fuu ; Chen, Jaw-Yeh ; Chao, Hung-Xin

  • Author_Institution
    Dept. of Electr. & Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan
  • Volume
    31
  • Issue
    6
  • fYear
    2001
  • fDate
    11/1/2001 12:00:00 AM
  • Firstpage
    645
  • Lastpage
    654
  • Abstract
    In the past, the estimation of crowd density has become an important topic in the field of automatic surveillance systems. In this paper, the developed system goes one step further to estimate the number of people in crowded scenes in a complex background by using a single image. Therefore, more valuable information than crowd density can be obtained. There are two major steps in this system: recognition of the head-like contour and estimation of crowd size. First, the Haar wavelet transform is used to extract the featured area of the head-like contour, and then the support vector machine is used to classify these featured area as the contour of a head or not. Next, the perspective transforming technique of computer vision is used to estimate crowd size more accurately. Finally, a model world is constructed to test this proposed system and the system is also applied for real-world images
  • Keywords
    computer vision; edge detection; feature extraction; neural nets; object recognition; surveillance; wavelet transforms; Haar wavelet transform; complex background; computer vision; contour recognition; crowd density estimation; crowd size; crowded scenes; feature extraction; perspective transform; support vector machine; surveillance; Computer vision; Data mining; Feature extraction; Layout; Magnetic heads; Support vector machine classification; Support vector machines; Surveillance; System testing; Wavelet transforms;
  • fLanguage
    English
  • Journal_Title
    Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1083-4427
  • Type

    jour

  • DOI
    10.1109/3468.983420
  • Filename
    983420