An effective subsuperpixel-based approach for background subtraction

Yu Qiu Chen, Zhan Li Sun, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

29 Citations (Scopus)


How to achieve competitive accuracy and less computation time simultaneously for background estimation is still an intractable task. In this paper, an effective background subtraction approach for video sequences is proposed based on a subsuperpixel model. In our algorithm, the superpixels of the first frame are constructed using a simple linear iterative clustering method. After transforming the frame from a color format to gray level, the initial superpixels are divided into K smaller units, i.e., subsuperpixels, via the k-means clustering algorithm. The background model is then initialized by representing each subsuperpixel as a multidimensional feature vector. For the subsequent frames, moving objects are detected by the subsuperpixel representation and a weighting measure. In order to deal with ghost artifacts, a background model updating strategy is devised, based on the number of pixels represented by each cluster center. As each superpixel is refined via the subsuperpixel representation, the proposed method is more efficient and achieves a competitive accuracy for background subtraction. Experimental results demonstrate the effectiveness of the proposed method.

Original languageEnglish
Article number8624606
Pages (from-to)601-609
Number of pages9
JournalIEEE Transactions on Industrial Electronics
Issue number1
Publication statusPublished - Jan 2020


  • Background subtraction
  • k-means clustering
  • superpixel

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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