Motion-based video segmentation using fuzzy clustering and classical mixture model

S. Nitsuwat*, J. S. Jin, H. M. Hudson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)

Abstract

Motion-based segmentation plays an important role in dynamic scene analysis of video sequences. In this paper, we present a scheme for extracting moving objects. First, three different resolutions of the dense optical flow fields are calculated using a complex discrete wavelet transform. Surface fitting of all levels of these vectors is then performed over the affine parametric motion model. Next, the clustering by Competitive Agglomeration algorithm is applied in the parameter space of the coarsest level. The results of this step are the optimum number of clusters and the center of each cluster. Using information from the previous level, the parameter spaces of the following levels are then segmented using the classical mixture model and the expectation-maximization algorithm. Finally, the individual moving object and background are represented in layers. Experimental results showing the significance of this proposed method are provided.

Original languageEnglish
Title of host publicationIEEE International Conference on Image Processing
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages300-303
Number of pages4
Volume1
Publication statusPublished - 2000
Externally publishedYes
Event2000 International Conference on Image Processing - Vancouver, Canada
Duration: 10 Sep 200013 Sep 2000

Conference

Conference2000 International Conference on Image Processing
CityVancouver, Canada
Period10/09/0013/09/00

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