Internet of moving target detection method based on nonparametric background model
In traffic surveillance system, mobile target detection and identification is the key technology in traffic surveillance system. In this paper, one detection method based on non-parametric background model is adopted on the basis of the summary of previous background modeling. In the model, a series...
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| Veröffentlicht in: | International journal of computers & applications 2021-02, Vol.43 (2), p.193-198 |
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| container_title | International journal of computers & applications |
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| creator | Hongli, Li Yaofeng, Ma |
| description | In traffic surveillance system, mobile target detection and identification is the key technology in traffic surveillance system. In this paper, one detection method based on non-parametric background model is adopted on the basis of the summary of previous background modeling. In the model, a series of sampling values are used to estimate and observe probability model of pixel points; and then, the probability model is used for binarization detection of mobile targets. In the end, we have brought favorable detection effects by noise suppression treatment. As for identification of mobile targets, several features are proposed in this paper and neural network is used for identification training. Experiment results show that classification of pedestrian and vehicle targets according to these features has a high rate of identification. |
| doi_str_mv | 10.1080/1206212X.2018.1537096 |
| format | Article |
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Experiment results show that classification of pedestrian and vehicle targets according to these features has a high rate of identification.</description><subject>background model</subject><subject>Moving targets</subject><subject>Neural networks</subject><subject>Noise reduction</subject><subject>Nonparametric statistics</subject><subject>Proposal of features</subject><subject>Surveillance</subject><subject>Target detection</subject><subject>target identification</subject><subject>Target recognition</subject><subject>Traffic models</subject><subject>Traffic surveillance</subject><subject>video surveillance</subject><issn>1206-212X</issn><issn>1925-7074</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9UNtKAzEQDaJgrX6CsODz1kn2kt03pXgpFARR8C3M5lK37iY1SZX-vSnVV59m5nDOmZlDyCWFGYUGrimDmlH2NmNAmxmtCg5tfUQmtGVVzoGXx6lPnHxPOiVnIawBSs7qZkKeFzZqb3XMnMlG99XbVRbRrxKgdNQy9s5mo47vTmUdBq2yNFtnN-gxwb6XCZYfK--2ViUDpYdzcmJwCPrit07J6_3dy_wxXz49LOa3y1ymO2OObVMC6zjjTYGoZINQdchRUsNAmY5DJ0tdFIZi12Lb8k6ihFJpo6CRCospuTr4brz73OoQxdptvU0rBavKsqjq9GViVQeW9C4Er43Y-H5EvxMUxD4-8Ref2McnfuNLupuDrrfG-RG_nR-UiLgbnDcereyDKP63-AEQCHj0</recordid><startdate>20210207</startdate><enddate>20210207</enddate><creator>Hongli, Li</creator><creator>Yaofeng, Ma</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20210207</creationdate><title>Internet of moving target detection method based on nonparametric background model</title><author>Hongli, Li ; Yaofeng, Ma</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c201t-a98402b72783aadc8a05ba7ac1f20dfb70bc4e33f1ab9a997bcac04defd08cda3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>background model</topic><topic>Moving targets</topic><topic>Neural networks</topic><topic>Noise reduction</topic><topic>Nonparametric statistics</topic><topic>Proposal of features</topic><topic>Surveillance</topic><topic>Target detection</topic><topic>target identification</topic><topic>Target recognition</topic><topic>Traffic models</topic><topic>Traffic surveillance</topic><topic>video surveillance</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hongli, Li</creatorcontrib><creatorcontrib>Yaofeng, Ma</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>International journal of computers & applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hongli, Li</au><au>Yaofeng, Ma</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Internet of moving target detection method based on nonparametric background model</atitle><jtitle>International journal of computers & applications</jtitle><date>2021-02-07</date><risdate>2021</risdate><volume>43</volume><issue>2</issue><spage>193</spage><epage>198</epage><pages>193-198</pages><issn>1206-212X</issn><eissn>1925-7074</eissn><abstract>In traffic surveillance system, mobile target detection and identification is the key technology in traffic surveillance system. 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| ispartof | International journal of computers & applications, 2021-02, Vol.43 (2), p.193-198 |
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| language | eng |
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| source | Taylor & Francis Online |
| subjects | background model Moving targets Neural networks Noise reduction Nonparametric statistics Proposal of features Surveillance Target detection target identification Target recognition Traffic models Traffic surveillance video surveillance |
| title | Internet of moving target detection method based on nonparametric background model |
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