Open AccessRobust foreground modelling to segment and detect multiple moving objects in videos
Author(s) -
Rahul Patil,
K P Chethan,
Azra Nasreen,
G Shobha
Publication year - 2020
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v10i2.pp1337-1345
Subject(s) - computer science , artificial intelligence , computer vision , segmentation , object detection , de facto , market segmentation , foreground detection , object (grammar) , noise (video) , image (mathematics) , marketing , political science , law , business
Last decade has witnessed an ever increasing number of video surveillance installations due to the rise of security concerns worldwide. With this comes the need for video analysis for fraud detection, crime investigation, traffic monitoring to name a few. For any kind of video analysis application, detection of moving objects in videos is a fundamental step. In this paper, an efficient foreground modelling method to segment multiple moving objects is implemented. Proposed method significantly reduces noise thereby accurately segmenting region of interest under dynamic conditions while handling occlusion to a large extent. Extensive performance analysis shows that the proposed method was found to give far better results when compared to the de facto standard as well as relatively new approaches used for
moving object detection.