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Parallel Key Frame Extraction for Surveillance Video Service in a Smart City
Surveillance video service (SVS) is one of the most important services provided in a smart city. It is very important for the utilization of SVS to provide design efficient surveillance video analysis techniques. Key frame extraction is a simple yet effective technique to achieve this goal. In surve...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4540463/ https://www.ncbi.nlm.nih.gov/pubmed/26284923 http://dx.doi.org/10.1371/journal.pone.0135694 |
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author | Zheng, Ran Yao, Chuanwei Jin, Hai Zhu, Lei Zhang, Qin Deng, Wei |
author_facet | Zheng, Ran Yao, Chuanwei Jin, Hai Zhu, Lei Zhang, Qin Deng, Wei |
author_sort | Zheng, Ran |
collection | PubMed |
description | Surveillance video service (SVS) is one of the most important services provided in a smart city. It is very important for the utilization of SVS to provide design efficient surveillance video analysis techniques. Key frame extraction is a simple yet effective technique to achieve this goal. In surveillance video applications, key frames are typically used to summarize important video content. It is very important and essential to extract key frames accurately and efficiently. A novel approach is proposed to extract key frames from traffic surveillance videos based on GPU (graphics processing units) to ensure high efficiency and accuracy. For the determination of key frames, motion is a more salient feature in presenting actions or events, especially in surveillance videos. The motion feature is extracted in GPU to reduce running time. It is also smoothed to reduce noise, and the frames with local maxima of motion information are selected as the final key frames. The experimental results show that this approach can extract key frames more accurately and efficiently compared with several other methods. |
format | Online Article Text |
id | pubmed-4540463 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-45404632015-08-24 Parallel Key Frame Extraction for Surveillance Video Service in a Smart City Zheng, Ran Yao, Chuanwei Jin, Hai Zhu, Lei Zhang, Qin Deng, Wei PLoS One Research Article Surveillance video service (SVS) is one of the most important services provided in a smart city. It is very important for the utilization of SVS to provide design efficient surveillance video analysis techniques. Key frame extraction is a simple yet effective technique to achieve this goal. In surveillance video applications, key frames are typically used to summarize important video content. It is very important and essential to extract key frames accurately and efficiently. A novel approach is proposed to extract key frames from traffic surveillance videos based on GPU (graphics processing units) to ensure high efficiency and accuracy. For the determination of key frames, motion is a more salient feature in presenting actions or events, especially in surveillance videos. The motion feature is extracted in GPU to reduce running time. It is also smoothed to reduce noise, and the frames with local maxima of motion information are selected as the final key frames. The experimental results show that this approach can extract key frames more accurately and efficiently compared with several other methods. Public Library of Science 2015-08-18 /pmc/articles/PMC4540463/ /pubmed/26284923 http://dx.doi.org/10.1371/journal.pone.0135694 Text en © 2015 Zheng et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Zheng, Ran Yao, Chuanwei Jin, Hai Zhu, Lei Zhang, Qin Deng, Wei Parallel Key Frame Extraction for Surveillance Video Service in a Smart City |
title | Parallel Key Frame Extraction for Surveillance Video Service in a Smart City |
title_full | Parallel Key Frame Extraction for Surveillance Video Service in a Smart City |
title_fullStr | Parallel Key Frame Extraction for Surveillance Video Service in a Smart City |
title_full_unstemmed | Parallel Key Frame Extraction for Surveillance Video Service in a Smart City |
title_short | Parallel Key Frame Extraction for Surveillance Video Service in a Smart City |
title_sort | parallel key frame extraction for surveillance video service in a smart city |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4540463/ https://www.ncbi.nlm.nih.gov/pubmed/26284923 http://dx.doi.org/10.1371/journal.pone.0135694 |
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