Open AccessCVABS: moving object segmentation with common vector approach for videos
Author(s) -
Işık Şahin,
Özkan Kemal,
Nezih Gerek Ömer
Publication year - 2019
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5642
Subject(s) - computer science , artificial intelligence , subspace topology , computer vision , segmentation , object detection , object (grammar) , background image , foreground detection , pattern recognition (psychology) , variety (cybernetics) , background subtraction , support vector machine , image segmentation , image (mathematics) , pixel
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.