IFTrace: Video segmentation of deformable objects using the Image Foresting Transform

  • Authors:

  • R. Minetto;T. V. Spina;A. X. Falcão;N. J. Leite;J. P. Papa;J. Stolfi

  • Affiliations:

  • Institute of Computing, University of Campinas - UNICAMP, Campinas, Brazil;Institute of Computing, University of Campinas - UNICAMP, Campinas, Brazil;Institute of Computing, University of Campinas - UNICAMP, Campinas, Brazil;Institute of Computing, University of Campinas - UNICAMP, Campinas, Brazil;Department of Computing, University Estadual Paulista - UNESP, Bauru, Brazil;Institute of Computing, University of Campinas - UNICAMP, Campinas, Brazil

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2012

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Abstract

We introduce IFTrace, a method for video segmentation of deformable objects. The algorithm makes minimal assumptions about the nature of the tracked object: basically, that it consists of a few connected regions, and has a well-defined border. The objects to be tracked are interactively segmented in the first frame of the video, and a set of markers is then automatically selected in the interior and immediate surroundings of the object. These markers are then located in the next frame by a combination of KLT feature finding and motion extrapolation. Object boundaries are then identified from these markers by the Image Foresting Transform (IFT). These steps are repeated for all subsequent frames until the end of the movie. Thanks to the IFT and a special boundary detection operator, IFTrace can reliably track deformable objects in the presence of partial and total occlusions, camera motion, lighting and color changes, and other complications. Tests on real videos show that the IFT is better suited to this task than Graph-Cut methods, and that IFTrace is more robust than other state-of-the art algorithms - namely, the OpenCV Snake and CamShift algorithms, Hess's Particle-Filter, and Zhong and Chang's method based on spatio-temporal consistency.