• DocumentCode
    2127769
  • Title

    Tracking moving objects with co-evolutionary snakes

  • Author

    Liatsis, P. ; Ooi, C.

  • Author_Institution
    Dept. of Electr. Eng. & Electron., Univ. of Manchester Inst. of Sci. & Technol., UK
  • fYear
    2002
  • fDate
    2002
  • Firstpage
    325
  • Lastpage
    332
  • Abstract
    A new symbiotic genetic algorithm (SGA) based active contour model (snake) is proposed to track the B-spline contour of obstacles. It exploits the local control properties of the B-spline to decompose the contour into subcontours and optimizes each subcontour in separate genetic algorithms (GA). Unlike the GA-based snake, an SGA snake can track the obstacle´s outline more robustly. Application-specific inter-population genetic operators are introduced to reinforce the symbiotic relationship via migration of genetic material. The use of symbiosis dramatically reduces the combinatorics of the search space, when compared to GAs. Results of tracking objects in real road scenarios demonstrate its robustness to noise and stability of convergence when compared to its GA counterpart.
  • Keywords
    convergence of numerical methods; genetic algorithms; image motion analysis; object detection; splines (mathematics); target tracking; B-spline contour; active contour model; image object location; inter-population genetic operators; moving object tracking; obstacle outline; real road scenarios; snake; symbiotic genetic algorithm; Active contours; Combinatorial mathematics; Control system synthesis; Deformable models; Genetic algorithms; Noise robustness; Robust stability; Shape control; Spline; Symbiosis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Video/Image Processing and Multimedia Communications 4th EURASIP-IEEE Region 8 International Symposium on VIPromCom
  • Print_ISBN
    953-7044-01-7
  • Type

    conf

  • DOI
    10.1109/VIPROM.2002.1026677
  • Filename
    1026677