A novel Mean Shift algorithm combined with Least Square approach and its application in target tracking
In this paper, a Mean Shift algorithm based on Least Square prediction is proposed. Based on the continuity of target's trace, the Least Square Mean Shift (LSMS) algorithm uses the result of Least Square prediction as the initial search center of Mean Shift algorithm. Then, Mean Shift is applie...
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| creator | Yongwei Zheng Huiyuan Wang Qianxi Guo |
| description | In this paper, a Mean Shift algorithm based on Least Square prediction is proposed. Based on the continuity of target's trace, the Least Square Mean Shift (LSMS) algorithm uses the result of Least Square prediction as the initial search center of Mean Shift algorithm. Then, Mean Shift is applied to get the final target position. The computational complexity is reduced by limiting the number of iterations of Mean shift. Experimental results show that, compared with traditional Mean Shift algorithm, the proposed algorithm improves the real-time realization. Meanwhile, it has better performance on fast moving targets and non-linear moving targets. |
| doi_str_mv | 10.1109/ICoSP.2012.6491770 |
| format | Conference Proceeding |
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Based on the continuity of target's trace, the Least Square Mean Shift (LSMS) algorithm uses the result of Least Square prediction as the initial search center of Mean Shift algorithm. Then, Mean Shift is applied to get the final target position. The computational complexity is reduced by limiting the number of iterations of Mean shift. Experimental results show that, compared with traditional Mean Shift algorithm, the proposed algorithm improves the real-time realization. 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Meanwhile, it has better performance on fast moving targets and non-linear moving targets.</description><subject>Least Square Method</subject><subject>LSMS</subject><subject>Mean Shift</subject><subject>Target Tracking</subject><issn>2164-5221</issn><isbn>9781467321969</isbn><isbn>1467321966</isbn><isbn>1467321974</isbn><isbn>9781467321952</isbn><isbn>1467321958</isbn><isbn>9781467321976</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kM1OAjEYRWvUREReQDd9gcF-_e-SEEUSjCbomrSdzlAdOtipGt9ejbg6OWdxFxehSyBTAGKul_N-_TilBOhUcgNKkSN0DlwqRsEofowmRul_l-YEjShIXglK4QxNhuGFEMJAa8nkCLUznPqP0OH7YBNeb2NTsO3aPsey3WHf71xMocafP4pXwQ4Fr9_ebQ7Y7ve5t36LbapxLMNv6KK3JfYJx4SLzW0ouGTrX2NqL9BpY7shTA4co-fbm6f5XbV6WCzns1UVQYlSWdC1kQwaQ6iTTlOnKHEehAfHhG5k7TinDdOMCkKElN4LQRkDIhpORWBjdPW3G0MIm32OO5u_Noef2DeaXlmh</recordid><startdate>201210</startdate><enddate>201210</enddate><creator>Yongwei Zheng</creator><creator>Huiyuan Wang</creator><creator>Qianxi Guo</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201210</creationdate><title>A novel Mean Shift algorithm combined with Least Square approach and its application in target tracking</title><author>Yongwei Zheng ; Huiyuan Wang ; Qianxi Guo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-a18d9631f902b6b82b720bc15c1b358f6db442f3832500566cc55233105f425e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Least Square Method</topic><topic>LSMS</topic><topic>Mean Shift</topic><topic>Target Tracking</topic><toplevel>online_resources</toplevel><creatorcontrib>Yongwei Zheng</creatorcontrib><creatorcontrib>Huiyuan Wang</creatorcontrib><creatorcontrib>Qianxi Guo</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yongwei Zheng</au><au>Huiyuan Wang</au><au>Qianxi Guo</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A novel Mean Shift algorithm combined with Least Square approach and its application in target tracking</atitle><btitle>2012 IEEE 11th International Conference on Signal Processing</btitle><stitle>ICoSP</stitle><date>2012-10</date><risdate>2012</risdate><volume>2</volume><spage>1102</spage><epage>1105</epage><pages>1102-1105</pages><issn>2164-5221</issn><isbn>9781467321969</isbn><isbn>1467321966</isbn><eisbn>1467321974</eisbn><eisbn>9781467321952</eisbn><eisbn>1467321958</eisbn><eisbn>9781467321976</eisbn><abstract>In this paper, a Mean Shift algorithm based on Least Square prediction is proposed. Based on the continuity of target's trace, the Least Square Mean Shift (LSMS) algorithm uses the result of Least Square prediction as the initial search center of Mean Shift algorithm. Then, Mean Shift is applied to get the final target position. The computational complexity is reduced by limiting the number of iterations of Mean shift. Experimental results show that, compared with traditional Mean Shift algorithm, the proposed algorithm improves the real-time realization. Meanwhile, it has better performance on fast moving targets and non-linear moving targets.</abstract><pub>IEEE</pub><doi>10.1109/ICoSP.2012.6491770</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2012 IEEE 11th International Conference on Signal Processing, 2012, Vol.2, p.1102-1105 |
| issn | 2164-5221 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Least Square Method LSMS Mean Shift Target Tracking |
| title | A novel Mean Shift algorithm combined with Least Square approach and its application in target tracking |
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