Using Harris Corner Detection and Background Modeling for InPainting Occluded Objects in a Video

  • Authors

    • Israa Hadi Ali
    • Mustafa Ehsan M
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.19.27983
  • Video inpainting, Background model, Remove object, Video editing, Video tracking, occlusion.

  • Abstract

    In this papera proposed ofan efficient method for video inpainting which can be used to remove a single unwanted object that obscured another wanted object in a period of time in a video scene that captured for two occluded objects only. The proposed system based on statistical modified background modeling and interest point detection for the wanted object. Occlusion is the most important challenge in video inpainting and the proposed method can handle this challenge with a good result and less computational cost.The system consists of the following main steps. Firstly, input the original video then estimate the background of the video. After that detect the foreground objects by subtraction the background. Then applying segment and labeling stage each foreground object. Followed by tracking the selected two objects. Thenanalysis the occluded frames with computing interest point for the wanted object in the non-occluded frames basing on Harris corner detector. The final stage filling the unwanted object pixels by two choices either background model pixels or wanted object pixels.The proposed system focus on interruption cases that may be happened by the unwanted object and blocking another wanted and important object. By applying this methodology, the process of inpainting the unwanted object with the keeping of consistency of video.

     



     

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  • How to Cite

    Hadi Ali, I., & Ehsan M, M. (2018). Using Harris Corner Detection and Background Modeling for InPainting Occluded Objects in a Video. International Journal of Engineering & Technology, 7(4.19), 681-687. https://doi.org/10.14419/ijet.v7i4.19.27983

    Received date: 2019-02-26

    Accepted date: 2019-02-26

    Published date: 2018-11-27