An effective foreground segmentation using adaptive region based background modelling
| Date | 31 January 2020 |
| DOI | https://doi.org/10.1108/IDD-01-2019-0010 |
| Pages | 23-34 |
| Published date | 31 January 2020 |
| Author | Shahidha Banu S.,Maheswari N. |
| Subject Matter | Library & information science,Library & information services,Lending,Document delivery,Collection building & management,Stock revision,Consortia |
An effective foreground segmentation using
adaptive region based background modelling
Shahidha Banu S. and Maheswari N.
School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India
Abstract
Purpose –Background modelling has played an imperative role in the moving object detection as the progress of foreground extraction during
video analysis and surveillance in many real-time applications. It is usually done by background subtraction. This method is uprightly based on a
mathematical model with a fixed feature as a static background, where the background image is fixed with the foreground object running over it.
Usually, this image is taken as the background model and is compared against every new frame of the input video seq uence. In this paper, the
authors presented a renewed background modelling method for foreground segmentation. The principal objective of the work is to perform the
foreground object detection only in the premeditated region of interest (ROI). The ROI is calculated using the proposed algo rithm reducing and
raising by half (RRH). In this algorithm, the coordinate of a circle with the frame width as the diameter is considered for traversal to find the pixel
difference. The change in the pixel intensity is considered to be the foreground object and the position of it is determined based on the pixel
location. Most of the techniques study their updates to the pixels of the complete frame which may result in increased false rate; The proposed
system deals these flaw by controlling the ROI object (the region only where the background subtraction is performed) and thus extracts a corre ct
foreground by exactly categorizes the pixel as the foreground and mines the precise foreground object. The broad experimental results and the
evaluation parameters of the proposed approach with the state of art methods were compared against the most recent background subtraction
approaches. Moreover, the efficiency of the authors’method is analyzed in different situations to prove that this method is available for real-time
videos as well as videos available in the 2014 challenge change detection data set.
Design/methodology/approach –In this paper, the authors presented a fresh background modelling method for foreground segmentation. The
main objective of the work is to perform the foreground object detection only on the premeditated ROI. The region for foreground extraction is
calculated using proposed RRH algorithm. Most of the techniques study their updates to the pixels of the complete frame which may result in
increased false rate; most challenging case is that, the slow moving object is updated quickly to detect the foreground region. The anticipated
system deals these flaw by controlling the ROI object (the region only where the background subtraction is performed) and thus extracts a corre ct
foreground by exactly categorizing the pixel as the foreground and mining the precise foreground object.
Findings –Plum Analytics provide a new conduit for documenting and contextualizing the public impact and reach of research within digitally
networked environments. While limitations are notable, the metrics promoted through the platform can be used to build a more comprehe nsive
view of research impact.
Originality/value –The algorithm used in the work was proposed by the authors and are used for experimental evaluations.
Keywords Image processing, Background modelling, Background subtraction, Foreground segmentation, Intrusion detection system,
Region-based background modelling
Paper type Research paper
1. Introduction
The significant increase in the number of cameras for the
purpose of video surveillance is due to low cost of higher
resolution cameras. However, some issues always hold back
users related to the surveillance of the captured videos, for
example the cost will be more as we maintain the recorded data
a long duration. Thus it is required for a motion detection
algorithm which forms basic working of background
subtraction, which can further build a background model to
which the current frameis compared.
The background subtraction algorithmis therefore required,
to discriminate foregroundfrom background. The static part of
the scene here is background and the moving objects are the
foreground. While a static background model is enough to
analyse short videos in a controlled indoor environment, it is
insufficient for most practical cases; Therefore, a more refined
model is required to handle dynamic background, background
with shadow and illumination change. Moreover, the motion
detection is only the preliminary step in any of the application
related to surveillance. For example, finding the region where
Thecurrentissueandfulltextarchiveofthisjournalisavailableon
Emerald Insight at: https://www.emerald.com/insight/2398-6247.htm
Information Discovery and Delivery
48/1 (2020) 23–34
© Emerald Publishing Limited [ISSN 2398-6247]
[DOI 10.1108/IDD-01-2019-0010]
We thank the team who made the website (www.changedetection.net)
available and for providing the resource to test and compare our method
with other state of art methods.
Received 31 January 2019
Revised 11 March 2019
19 April 2019
9 July 2019
28 August 2019
Accepted 6 September 2019
23
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