Motion segmentation-based surveillance video compression using adaptive particle swarm optimization

Video surveillance is one of the widely used and most active research applications of computer vision. Although lots of works have been done in the area of smart surveillance, but still there is a need of effective compression technique for compact archival and efficient transmission of vast amount...

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Published in:	Neural computing & applications Vol. 32; no. 15; pp. 11443 - 11457
Main Authors:	Sengar, Sandeep Singh, Mukhopadhyay, Susanta
Format:	Journal Article
Language:	English
Published:	London Springer London 01.08.2020 Springer Nature B.V
Subjects:	Artificial Intelligence Change detection Coding Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer vision Data Mining and Knowledge Discovery Huffman codes Image Processing and Computer Vision Image segmentation Motion compensation Motion simulation Optical flow (image analysis) Original Article Particle swarm optimization Probability and Statistics in Computer Science Segmentation Surveillance Video compression Video data Video transmission Segmentation Motion estimation Particle swarm optimization Optical flow
ISSN:	0941-0643, 1433-3058
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Abstract	Video surveillance is one of the widely used and most active research applications of computer vision. Although lots of works have been done in the area of smart surveillance, but still there is a need of effective compression technique for compact archival and efficient transmission of vast amount of surveillance video data. In this work, we propose a hybrid video compression approach with the help of foreground motion compensation for the above application. This method works effectively by including the advantages of both block-based and object-based coding techniques as well as reducing the drawbacks of both. The proposed method first segments the foreground moving objects from the background with the help of adaptive thresholding-based optical flow techniques. Next, it determines the contour of the segmented foreground regions with the help of Freeman chain code. Subsequently, block-based motion estimation and compensation using variants of particle swarm optimization are computed. After that, motion failure areas are detected using change detection method, and finally, DCT and Huffman coding-based entropy encoding are done to compactly represent the data. Experimental results and analyses on different surveillance video sequences using Wilcoxon’s rank-sum test, PSNR and SSID show that our method outperforms other recent and relevant existing techniques.
AbstractList	Video surveillance is one of the widely used and most active research applications of computer vision. Although lots of works have been done in the area of smart surveillance, but still there is a need of effective compression technique for compact archival and efficient transmission of vast amount of surveillance video data. In this work, we propose a hybrid video compression approach with the help of foreground motion compensation for the above application. This method works effectively by including the advantages of both block-based and object-based coding techniques as well as reducing the drawbacks of both. The proposed method first segments the foreground moving objects from the background with the help of adaptive thresholding-based optical flow techniques. Next, it determines the contour of the segmented foreground regions with the help of Freeman chain code. Subsequently, block-based motion estimation and compensation using variants of particle swarm optimization are computed. After that, motion failure areas are detected using change detection method, and finally, DCT and Huffman coding-based entropy encoding are done to compactly represent the data. Experimental results and analyses on different surveillance video sequences using Wilcoxon’s rank-sum test, PSNR and SSID show that our method outperforms other recent and relevant existing techniques.
Author	Mukhopadhyay, Susanta Sengar, Sandeep Singh
Author_xml	– sequence: 1 givenname: Sandeep Singh orcidid: 0000-0003-2171-9332 surname: Sengar fullname: Sengar, Sandeep Singh email: sandeepsingh.s@srmap.edu.in organization: Department of Computer Science and Engineering, SRM University-AP – sequence: 2 givenname: Susanta surname: Mukhopadhyay fullname: Mukhopadhyay, Susanta organization: Department of Computer Science and Engineering, Indian Institute of Technology (ISM)
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Snippet	Video surveillance is one of the widely used and most active research applications of computer vision. Although lots of works have been done in the area of...
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SubjectTerms	Artificial Intelligence Change detection Coding Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer vision Data Mining and Knowledge Discovery Huffman codes Image Processing and Computer Vision Image segmentation Motion compensation Motion simulation Optical flow (image analysis) Original Article Particle swarm optimization Probability and Statistics in Computer Science Segmentation Surveillance Video compression Video data Video transmission
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Title	Motion segmentation-based surveillance video compression using adaptive particle swarm optimization
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