Soft Computing

, Volume 20, Issue 2, pp 785–805 | Cite as

Efficient silhouette-based contour tracking using local information

  • Ajoy Mondal
  • Susmita Ghosh
  • Ashish Ghosh
Methodologies and Application


In this article, we present an algorithm that can efficiently track the contour extracted from silhouette of the moving object of a given video sequence using local neighborhood information and fuzzy k-nearest-neighbor classifier. To classify each unlabeled sample in the target frame, instead of considering the whole training set, a subset of it is considered depending on the amount of motion of the object between immediate previous two consecutive frames. This technique makes the classification process faster and may increase the classification accuracy. Classification of the unlabeled samples in the target frame provides object (silhouette of the object) and background (non-object) regions. Transition pixels from the non-object region to the object silhouette and vice versa are treated as the boundary or contour pixels of the object. Contour or boundary of the object is extracted by connecting the boundary pixels and the object is tracked with this contour in the target frame. We show a realization of the proposed method and demonstrate it on eight benchmark video sequences. The effectiveness of the proposed method is established by comparing it with six state of the art contour tracking techniques, both qualitatively and quantitatively.


Fuzzy k-nearest-neighbor classifier Boundary pixels  Contour tracking Motion 



The authors like to thank the reviewers for their thorough and constructive comments, which helped a lot to enhance the quality of the manuscript. Funding by U. S. Army through the project “Processing and Analysis of Aircraft Images with Machine Learning Techniques for Locating Objects of Interest” (Contract No. FA5209-08-P-0241) is also gratefully acknowledged.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Machine Intelligence UnitIndian Statistical InstituteKolkataIndia
  2. 2.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia

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