CVABS: moving object segmentation with common vector approach for videos

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IŞIK Ş., ÖZKAN K., Gerek O. N.

IET COMPUTER VISION, vol.13, no.8, pp.719-729, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 13 Issue: 8
  • Publication Date: 2019
  • Doi Number: 10.1049/iet-cvi.2018.5642
  • Journal Name: IET COMPUTER VISION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.719-729
  • Keywords: computer vision, object detection, video signal processing, image motion analysis, image segmentation, smart foreground detection, background updating procedure, dynamic control parameters, dynamic backgrounds, objective measures, background frames, object segmentation, common vector approach, real-time computer vision applications, security systems, subspace-based background-modelling algorithm, CDNet2014 dataset, Gram-Schmidt orthogonalisation, self-learning feedback mechanism, MULTISCALE, TRACKING
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

Background modelling is a fundamental step for several real-time computer vision applications that requires security systems and monitoring. An accurate background model helps to detect the activity of moving objects in the video. In this work, the authors have developed a new subspace-based background-modelling algorithm using the concept of common vector approach (CVA) with Gram-Schmidt orthogonalisation. Once the background model that involves the common characteristic of different views corresponding to the same scene is acquired, a smart foreground detection and background updating procedure is applied based on dynamic control parameters. A variety of experiments is conducted on different problem types related to dynamic backgrounds. Several types of metrics are utilised as objective measures and the obtained visual results are judged subjectively. It was observed that the proposed method stands successfully for all problem types reported on CDNet2014 dataset by updating the background frames with a self-learning feedback mechanism.