Separating Moving Objects from Stationary Background using Dynamic Mode Decomposition

Athisha S.¹ , Keerthi Krishnan K.² , Sreelekshmi P.S.³

¹ Department of ECE, NSS College of Engineering, APJ Abdul Kalam Technological University, Palakkad, India.
² Department of ECE, NSS College of Engineering, APJ Abdul Kalam Technological University, Palakkad, India.
³ Department of ECE, NSS College of Engineering, APJ Abdul Kalam Technological University, Palakkad, India.

Section:Research Paper, Product Type: Journal
Vol.6 , Issue.3 , pp.58-64, Jun-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrnsc/v6i3.5864

Online published on Jun 30, 2018

Copyright © Athisha S., Keerthi Krishnan K., Sreelekshmi P.S. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Real-time background/foreground separation of a video is necessary for detecting an object, identifying, tracking vehicle, as well as recognizing objects. Several algorithms were already found for background initialization and foreground detection. Recent examinations have presented a robust method, Dynamic Mode Decomposition (DMD) for separating video frames into a background (low-rank) model and foreground (sparse) segments. The full video stream first converted to frames and applied DMD on each frame for the separation of background/foreground objects. Since the method uses full video frames it had more computational complexity and difficult to analyze. For a better solution, this paper shows that the large continuous video stream first converted to frames and each frame breaks into segments and then DMD applied on the segment where the moving object or foreground is obtained. The issue using DMD is the burden of working with large information and now it can be easy work using the segmented video frames. The strength of DMD is demonstrated using a publicly available Scene Background Initialisation (SBI) dataset. The objective of this work is to obtain a background/foreground model from a video sequence where the background is filled with a number of foreground objects with less complexity. Finally compared the accuracy parameters between different background/foreground separation methods with DMD and shows the performance of DMD_Segmented is much better.

Key-Words / Index Term :
Background/Foreground Separation, SBI Dataset, Dynamic Mode Decomposition (DMD), Segmentation

References :
This paper has been demonstrated that the method of dynamic mode decomposition, typically used for evaluating the dynamics of complex systems, can be used for background/foreground separation in videos with visually appealing results and excellent computational efficiency. The issue using DMD is a load of working with an excess of information and it can be easy work using the segmented video frames. This work obtained a background/foreground model from a video sequence where the background is filled with a number of foreground objects with less complexity and compared the results of DMD with other methods.

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