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Foreground Segmentation in Video Sequences with a Dynamic Background


Foreground Segmentation in Video Sequences with a Dynamic Background

Chu, Tang, Ahmad, M. Omair ORCID: https://orcid.org/0000-0002-2924-6659 and Wang, Chunyan ORCID: https://orcid.org/0000-0002-4594-8378 (2018) Foreground Segmentation in Video Sequences with a Dynamic Background. In: CISP-BMEI 2018.

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Official URL: https://doi.org/10.1109/CISP-BMEI.2018.8633130


Segmentation of a moving foreground from video sequences, in the presence of a rapidly changing background, is a difficult problem. In this paper, a novel technique for an effective segmentation of the moving foreground from video sequences with a dynamic background is developed. The segmentation problem is treated as a problem of classifying the foreground and background pixels of a video frame using the color components of the pixels as multiple features of the images. The gray levels of the pixels and the hue and saturation level components in the HSV representation of the pixels of a frame are used to form a scalar-valued feature image. This feature image incorporating multiple features of the pixels is then used to devise a simple classification scheme in the framework of a support vector machine classifier. Unlike some other data classification approaches for foreground segmentation in which a priori knowledge of the shape and size of the moving foreground is essential, in the proposed method, training samples are obtained in an automatic manner. In order to assess the effectiveness of the proposed method, the new scheme is applied to a number of video sequences with a dynamic background and the results are compared with those obtained by using other existing methods. The subjective and objective results show the superiority of the proposed scheme in providing a segmented foreground binary mask that fits more closely with the corresponding ground truth mask than those obtained by the other methods do.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Conference or Workshop Item (Paper)
Authors:Chu, Tang and Ahmad, M. Omair and Wang, Chunyan
Date:October 2018
  • Natural Sciences and Engineering Research Council of Canada (NSERC)
  • Regroupement Strategique en Microelectronic du Quebec (ReSMiQ)
Digital Object Identifier (DOI):10.1109/cisp-bmei.2018.8633130
Keywords:Foreground segmentation; support vector machine (SVM); spatial-temporal feature; dynamic background
ID Code:985309
Deposited By: CHUNYAN WANG
Deposited On:25 Apr 2019 19:21
Last Modified:25 Apr 2019 19:21
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