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Mean shift track initiation algorithm based on Hough transform
To solve the problem of initiating tracks for multi-target in dense clutters environment, a Mean shift track initiation algorithm based on Hough transform is proposed. In the algorithm, firstly, hough transform is applied to transform observation points from input space, referred to as feature space...
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| creator | Lijun Zhou Weixin Xie Liangqun Li |
| description | To solve the problem of initiating tracks for multi-target in dense clutters environment, a Mean shift track initiation algorithm based on Hough transform is proposed. In the algorithm, firstly, hough transform is applied to transform observation points from input space, referred to as feature space into curves in a special parameter space; then a Mean shift clustering algorithm is executed to cluster the items gained in the parameter space, and the problem of peak seeking is also solved adaptively. Furthermore, a fuzzy influential factor, which is based on the vote number of accumulation matrix and distance between items in the parameter space and clustering center, is defined to design kernel function of Mean shift; thus clutters are removed more effectively. Experimental results show that proposed algorithm has high detection accuracy and can initiate tracks effectively. |
| doi_str_mv | 10.1109/ICOSP.2010.5657191 |
| format | conference_proceeding |
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In the algorithm, firstly, hough transform is applied to transform observation points from input space, referred to as feature space into curves in a special parameter space; then a Mean shift clustering algorithm is executed to cluster the items gained in the parameter space, and the problem of peak seeking is also solved adaptively. Furthermore, a fuzzy influential factor, which is based on the vote number of accumulation matrix and distance between items in the parameter space and clustering center, is defined to design kernel function of Mean shift; thus clutters are removed more effectively. 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Experimental results show that proposed algorithm has high detection accuracy and can initiate tracks effectively.</description><subject>Algorithm design and analysis</subject><subject>Clustering algorithms</subject><subject>Clutter</subject><subject>Hough transform</subject><subject>Kernel</subject><subject>Mean Shift Clustering</subject><subject>Signal processing algorithms</subject><subject>Target tracking</subject><subject>Track initiation</subject><subject>Transforms</subject><issn>2164-5221</issn><isbn>9781424458974</isbn><isbn>1424458978</isbn><isbn>9781424458998</isbn><isbn>9781424459001</isbn><isbn>1424459001</isbn><isbn>1424458994</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpVj81KAzEcxCNWsLT7AnrJC2zNP9-5CLKoLVQq1HvJbpJutLsrm3jw7V2xF-cyzPBjYBC6AbICIOZuU-32rytKpiykUGDgAhVGaeCUc6GN0Zf_suIzNKcgeSkohWtUpPROJgmqqJFzdP_ibY9TG0PGebTNB459zNHmOPTYno7DGHPb4dom7_BUrYevY_tL9ikMY7dEV8Geki_OvkD7p8e3al1ud8-b6mFbRlAil96QoBtPdHC1IzXjyoMCSxhtlCKEecmltbXmzsjGCzfRVDAXOICmki3Q7d9q9N4fPsfY2fH7cP7PfgDD3kv6</recordid><startdate>201010</startdate><enddate>201010</enddate><creator>Lijun Zhou</creator><creator>Weixin Xie</creator><creator>Liangqun Li</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201010</creationdate><title>Mean shift track initiation algorithm based on Hough transform</title><author>Lijun Zhou ; Weixin Xie ; Liangqun Li</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-e90f8ce08fdbd0b347e171a032c77003e646aab84d96ce5d0f8253df4118263</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Algorithm design and analysis</topic><topic>Clustering algorithms</topic><topic>Clutter</topic><topic>Hough transform</topic><topic>Kernel</topic><topic>Mean Shift Clustering</topic><topic>Signal processing algorithms</topic><topic>Target tracking</topic><topic>Track initiation</topic><topic>Transforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Lijun Zhou</creatorcontrib><creatorcontrib>Weixin Xie</creatorcontrib><creatorcontrib>Liangqun Li</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 Xplore (Online service)</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>Lijun Zhou</au><au>Weixin Xie</au><au>Liangqun Li</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Mean shift track initiation algorithm based on Hough transform</atitle><btitle>IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS</btitle><stitle>ICOSP</stitle><date>2010-10</date><risdate>2010</risdate><spage>1263</spage><epage>1266</epage><pages>1263-1266</pages><issn>2164-5221</issn><isbn>9781424458974</isbn><isbn>1424458978</isbn><eisbn>9781424458998</eisbn><eisbn>9781424459001</eisbn><eisbn>1424459001</eisbn><eisbn>1424458994</eisbn><abstract>To solve the problem of initiating tracks for multi-target in dense clutters environment, a Mean shift track initiation algorithm based on Hough transform is proposed. In the algorithm, firstly, hough transform is applied to transform observation points from input space, referred to as feature space into curves in a special parameter space; then a Mean shift clustering algorithm is executed to cluster the items gained in the parameter space, and the problem of peak seeking is also solved adaptively. Furthermore, a fuzzy influential factor, which is based on the vote number of accumulation matrix and distance between items in the parameter space and clustering center, is defined to design kernel function of Mean shift; thus clutters are removed more effectively. Experimental results show that proposed algorithm has high detection accuracy and can initiate tracks effectively.</abstract><pub>IEEE</pub><doi>10.1109/ICOSP.2010.5657191</doi><tpages>4</tpages></addata></record> |
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| identifier | ISSN: 2164-5221 |
| ispartof | IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, 2010, p.1263-1266 |
| issn | 2164-5221 |
| language | eng |
| recordid | cdi_ieee_primary_5657191 |
| source | IEEE Xplore All Conference Series |
| subjects | Algorithm design and analysis Clustering algorithms Clutter Hough transform Kernel Mean Shift Clustering Signal processing algorithms Target tracking Track initiation Transforms |
| title | Mean shift track initiation algorithm based on Hough transform |
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