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Improved target tracking algorithm based on Camshift
The accuracy of tracking based on Camshift would decrease due to the similarity between target color and background color or the target is obscured. For the above problems, improved target tracking algorithm based on Camshift is proposed in this paper. The Camshift algorithm is improved by using the...
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creator | Xiu, Chunbo Su, Xuemiao Pan, Xiaonan |
description | The accuracy of tracking based on Camshift would decrease due to the similarity between target color and background color or the target is obscured. For the above problems, improved target tracking algorithm based on Camshift is proposed in this paper. The Camshift algorithm is improved by using the contour features of the target, and Camshift search window is updated according to the contour feature of the target. Thus, interference of background and strong light is weakened. Kalman filtering algorithm is used to predict the motion state of the tracking target, enhancing the efficiency of tracking when the tracking target is obscured. Experiments show that Camshift is combined with the contour feature of target and make the tracking more effectively under the conditions of background. And the Kalman filtering algorithm is used to predict position of the target to make the tracking effectively when the target is obscured. |
doi_str_mv | 10.1109/CCDC.2018.8407900 |
format | conference_proceeding |
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For the above problems, improved target tracking algorithm based on Camshift is proposed in this paper. The Camshift algorithm is improved by using the contour features of the target, and Camshift search window is updated according to the contour feature of the target. Thus, interference of background and strong light is weakened. Kalman filtering algorithm is used to predict the motion state of the tracking target, enhancing the efficiency of tracking when the tracking target is obscured. Experiments show that Camshift is combined with the contour feature of target and make the tracking more effectively under the conditions of background. 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For the above problems, improved target tracking algorithm based on Camshift is proposed in this paper. The Camshift algorithm is improved by using the contour features of the target, and Camshift search window is updated according to the contour feature of the target. Thus, interference of background and strong light is weakened. Kalman filtering algorithm is used to predict the motion state of the tracking target, enhancing the efficiency of tracking when the tracking target is obscured. Experiments show that Camshift is combined with the contour feature of target and make the tracking more effectively under the conditions of background. And the Kalman filtering algorithm is used to predict position of the target to make the tracking effectively when the target is obscured.</description><subject>Camshift Tracking Algorithm</subject><subject>Filtering algorithms</subject><subject>Interference</subject><subject>Kalman Filtering Algorithm</subject><subject>Kalman filters</subject><subject>Mathematical model</subject><subject>Prediction algorithms</subject><subject>Target Contour</subject><subject>Target tracking</subject><issn>1948-9447</issn><isbn>1538612437</isbn><isbn>9781538612446</isbn><isbn>1538612445</isbn><isbn>9781538612439</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2018</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj81KxDAURqMgODP6AOImL9B6b3PbJEuJfwMDbnQ93DZpJzqdDmkQfHsHnNW3OJwDnxB3CCUi2AfnnlxZAZrSEGgLcCGWWCvTYEVKX4oFWjKFJdLXYjnPXwBNowAWgtbjMU0_wcvMaQhZ5sTddzwMkvfDlGLejbLl-cSng3Q8zrvY5xtx1fN-DrfnXYnPl-cP91Zs3l_X7nFTRNR1LrRmxd76ukJP3Oqe606zh1YDK1DYGuxN1yIygUWk2qvGgvJA1ckjUCtx_9-NIYTtMcWR0-_2_FD9AT2xQ_Y</recordid><startdate>201806</startdate><enddate>201806</enddate><creator>Xiu, Chunbo</creator><creator>Su, Xuemiao</creator><creator>Pan, Xiaonan</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201806</creationdate><title>Improved target tracking algorithm based on Camshift</title><author>Xiu, Chunbo ; Su, Xuemiao ; Pan, Xiaonan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-77a3ad9d521d4ab7fa5c7ad0b70a3031b81f8cb11a4091145d36903d0423ad403</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Camshift Tracking Algorithm</topic><topic>Filtering algorithms</topic><topic>Interference</topic><topic>Kalman Filtering Algorithm</topic><topic>Kalman filters</topic><topic>Mathematical model</topic><topic>Prediction algorithms</topic><topic>Target Contour</topic><topic>Target tracking</topic><toplevel>online_resources</toplevel><creatorcontrib>Xiu, Chunbo</creatorcontrib><creatorcontrib>Su, Xuemiao</creatorcontrib><creatorcontrib>Pan, Xiaonan</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</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>Xiu, Chunbo</au><au>Su, Xuemiao</au><au>Pan, Xiaonan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Improved target tracking algorithm based on Camshift</atitle><btitle>2018 Chinese Control And Decision Conference (CCDC)</btitle><stitle>CCDC</stitle><date>2018-06</date><risdate>2018</risdate><spage>4449</spage><epage>4454</epage><pages>4449-4454</pages><eissn>1948-9447</eissn><eisbn>1538612437</eisbn><eisbn>9781538612446</eisbn><eisbn>1538612445</eisbn><eisbn>9781538612439</eisbn><abstract>The accuracy of tracking based on Camshift would decrease due to the similarity between target color and background color or the target is obscured. For the above problems, improved target tracking algorithm based on Camshift is proposed in this paper. The Camshift algorithm is improved by using the contour features of the target, and Camshift search window is updated according to the contour feature of the target. Thus, interference of background and strong light is weakened. Kalman filtering algorithm is used to predict the motion state of the tracking target, enhancing the efficiency of tracking when the tracking target is obscured. Experiments show that Camshift is combined with the contour feature of target and make the tracking more effectively under the conditions of background. And the Kalman filtering algorithm is used to predict position of the target to make the tracking effectively when the target is obscured.</abstract><pub>IEEE</pub><doi>10.1109/CCDC.2018.8407900</doi><tpages>6</tpages></addata></record> |
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subjects | Camshift Tracking Algorithm Filtering algorithms Interference Kalman Filtering Algorithm Kalman filters Mathematical model Prediction algorithms Target Contour Target tracking |
title | Improved target tracking algorithm based on Camshift |
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