Background subtraction driven seeds selection for moving objects segmentation and matting

  • Authors:

  • Bineng Zhong;Yan Chen;Yewang Chen;Rongrong Ji;Ying Chen;Duansheng Chen;Hanzi Wang

  • Affiliations:

  • Department of Computer Science and Engineering, Huaqiao University, Jimei Road, No.668, Xiamen, Fujian, China and School of Information Science and Technology, Xiamen University, Xiamen, Fujian, C ...;Department of Computer Science and Engineering, Huaqiao University, Jimei Road, No.668, Xiamen, Fujian, China;Department of Computer Science and Engineering, Huaqiao University, Jimei Road, No.668, Xiamen, Fujian, China;Department of Electronic Engineering, Columbia University, NY, USA;Department of Basic Sciences, Beijing Electronic Science and Technology Institute, Beijing, China;Department of Computer Science and Engineering, Huaqiao University, Jimei Road, No.668, Xiamen, Fujian, China;School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China

  • Venue:
  • Neurocomputing
  • Year:
  • 2013

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Abstract

In this paper, we address the difficult task of moving objects segmentation and matting in dynamic scenes. Toward this end, we propose a new automatic way to integrate a background subtraction (BGS) and an alpha matting technique via a heuristic seeds selection scheme. Specifically, our method can be divided into three main steps. First, we use a novel BGS method as attention mechanisms, generating many possible foreground pixels by tuning it for low false-positives and false-negatives as much as possible. Second, a connected components algorithm is used to give the bounding boxes of the labeled foreground pixels. Finally, matting of the object associated to a given bounding box is performed using a heuristic seeds selection scheme. This matting task is guided by top-down knowledge. Experimental results demonstrate the efficiency and effectiveness of our method.