• DocumentCode
    1798656
  • Title

    A moving objects detection algorithm in video sequence

  • Author

    Mingyang Yang

  • Author_Institution
    Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai, China
  • fYear
    2014
  • fDate
    7-9 July 2014
  • Firstpage
    410
  • Lastpage
    413
  • Abstract
    Background subtraction and frame differencing algorithm are widely used approaches for detecting moving objects from video sequences. Many different methods have been proposed over the recent years and many related researchers can be confused about their benefits and limitations. In order to overcome this problem, this paper provides an improved algorithm based on these two techniques. It uses surendra background algorithm to get background model and uses frame differencing algorithm to update it, then extracts the moving objects from the fusion of images extracted from these two methods. Because the moving objects we used in the video are bees which always fly in high speed and they are very small, the moving target becomes difficult to extract. In order to solve this problem and get more moving information, the circle segmentation dynamic threshold is used in this paper. It is shown through the result that the method can satisfy the precision and accuracy in the video sequences without affecting the requirement of real-time.
  • Keywords
    feature extraction; image fusion; image segmentation; image sequences; object detection; video signal processing; background model; background subtraction algorithm; circle segmentation dynamic threshold; extracted image fusion; frame differencing algorithm; moving object detection algorithm; surendra background algorithm; video sequence; Computer vision; Heuristic algorithms; Image motion analysis; Image segmentation; Noise; Optical imaging; Video sequences; background subtraction; frame differencing; moving objects detecting; surendra background model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Audio, Language and Image Processing (ICALIP), 2014 International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4799-3902-2
  • Type

    conf

  • DOI
    10.1109/ICALIP.2014.7009826
  • Filename
    7009826