• DocumentCode
    3590034
  • Title

    Automatic Human Body Tracking and Modeling from Monocular Video Sequences

  • Author

    Chih-Chang Yu ; Jenq-Neng Hwang ; Gang-Feng Ho ; Chaur-Heh Hsieh

  • Author_Institution
    Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Chung-li, Taiwan
  • Volume
    1
  • fYear
    2007
  • Abstract
    In this paper we developed a system for automated human body tracking and modeling based on a monocular camera. In this system, eleven joint points including head, shoulder, hip, elbows, knees, hands and feet are extracted separately to build a 2D human body model. The head is extracted by analyzing negative minimum curvature (NMC) points on a parameterized silhouette. The torso, along with its angle and size, is determined by integrating multiple frame information with connectivity constraint. Hands and feet can be identified correctly based on a modified star skeleton approach and the nearest-neighbor tracking mechanism. The rest of joint points can also be located by taking advantage of the connectivity constraints. A successful construction of the proposed human body modeling will pave a critical foundation for further intelligent analysis in many applications, such as automated video surveillance system or systematic video understanding.
  • Keywords
    feature extraction; image sequences; video signal processing; automated video surveillance system; automatic human body tracking; eleven joint points; modified star skeleton approach; monocular video sequences; multiple frame information; nearest-neighbor tracking mechanism; negative minimum curvature points; systematic video understanding; Biological system modeling; Cameras; Data mining; Elbow; Hip; Humans; Joints; Knee; Shoulder; Video sequences; Human body modeling; intelligent analysis; negative minimum points;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
  • ISSN
    1520-6149
  • Print_ISBN
    1-4244-0727-3
  • Type

    conf

  • DOI
    10.1109/ICASSP.2007.366058
  • Filename
    4217230