Multiple player tracking in basketball court videos
To build a smart basketball court, one basic task is to track players with the aid of the basketball court monitoring. This task can be regarded as a special case of the multiple object tracking (MOT) problem. But different from it, the task puts request to the existing MOT methods with both good ac...
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| Veröffentlicht in: | Journal of real-time image processing 2020-12, Vol.17 (6), p.1811-1828 |
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| container_issue | 6 |
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| container_title | Journal of real-time image processing |
| container_volume | 17 |
| creator | Fu, Xubo Zhang, Kun Wang, Changgang Fan, Chao |
| description | To build a smart basketball court, one basic task is to track players with the aid of the basketball court monitoring. This task can be regarded as a special case of the multiple object tracking (MOT) problem. But different from it, the task puts request to the existing MOT methods with both good accuracy and operational efficiency (toward real time) in the new basketball scenario. To deal with this task, we make the following attempts: (1) Considering the differences between pedestrians and basketball players and the lack of corresponding dataset for basketball players tracking, we construct a new MOT dataset under the basketball court monitoring scene, to better evaluate MOT methods in our task and to help promote future research in the related task, (2) evaluating the performance of the several candidate MOT methods on the new dataset, and (3) proposing the issues to be further addressed in this specific scenario. |
| doi_str_mv | 10.1007/s11554-020-00968-x |
| format | Article |
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This task can be regarded as a special case of the multiple object tracking (MOT) problem. But different from it, the task puts request to the existing MOT methods with both good accuracy and operational efficiency (toward real time) in the new basketball scenario. To deal with this task, we make the following attempts: (1) Considering the differences between pedestrians and basketball players and the lack of corresponding dataset for basketball players tracking, we construct a new MOT dataset under the basketball court monitoring scene, to better evaluate MOT methods in our task and to help promote future research in the related task, (2) evaluating the performance of the several candidate MOT methods on the new dataset, and (3) proposing the issues to be further addressed in this specific scenario.</description><identifier>ISSN: 1861-8200</identifier><identifier>EISSN: 1861-8219</identifier><identifier>DOI: 10.1007/s11554-020-00968-x</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Accuracy ; Basketball ; Computer Graphics ; Computer Science ; Datasets ; Deep learning ; Image Processing and Computer Vision ; Methods ; Monitoring ; Multimedia Information Systems ; Multiple target tracking ; Neural networks ; Pattern Recognition ; Pedestrians ; Performance evaluation ; Players ; Sensors ; Signal,Image and Speech Processing ; Special Issue Paper</subject><ispartof>Journal of real-time image processing, 2020-12, Vol.17 (6), p.1811-1828</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020</rights><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-30e36188350eb16582e8a859ccd0af8e8c01e345cd8c5d71d79553cf4e325c393</citedby><cites>FETCH-LOGICAL-c319t-30e36188350eb16582e8a859ccd0af8e8c01e345cd8c5d71d79553cf4e325c393</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11554-020-00968-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918678002?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21387,27923,27924,33743,41487,42556,43804,51318,64384,64388,72340</link.rule.ids></links><search><creatorcontrib>Fu, Xubo</creatorcontrib><creatorcontrib>Zhang, Kun</creatorcontrib><creatorcontrib>Wang, Changgang</creatorcontrib><creatorcontrib>Fan, Chao</creatorcontrib><title>Multiple player tracking in basketball court videos</title><title>Journal of real-time image processing</title><addtitle>J Real-Time Image Proc</addtitle><description>To build a smart basketball court, one basic task is to track players with the aid of the basketball court monitoring. This task can be regarded as a special case of the multiple object tracking (MOT) problem. But different from it, the task puts request to the existing MOT methods with both good accuracy and operational efficiency (toward real time) in the new basketball scenario. To deal with this task, we make the following attempts: (1) Considering the differences between pedestrians and basketball players and the lack of corresponding dataset for basketball players tracking, we construct a new MOT dataset under the basketball court monitoring scene, to better evaluate MOT methods in our task and to help promote future research in the related task, (2) evaluating the performance of the several candidate MOT methods on the new dataset, and (3) proposing the issues to be further addressed in this specific scenario.</description><subject>Accuracy</subject><subject>Basketball</subject><subject>Computer Graphics</subject><subject>Computer Science</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Image Processing and Computer Vision</subject><subject>Methods</subject><subject>Monitoring</subject><subject>Multimedia Information Systems</subject><subject>Multiple target tracking</subject><subject>Neural networks</subject><subject>Pattern Recognition</subject><subject>Pedestrians</subject><subject>Performance evaluation</subject><subject>Players</subject><subject>Sensors</subject><subject>Signal,Image and Speech Processing</subject><subject>Special Issue Paper</subject><issn>1861-8200</issn><issn>1861-8219</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kDFPwzAQhS0EEqXwB5giMRvu7DqxR1QBRSpigdlynUuVNiTBTlH77zEEwcZ0N7z37t7H2CXCNQIUNxFRqRkHARzA5Jrvj9gEdY5cCzTHvzvAKTuLcQOQF7lUEyafds1Q9w1lfeMOFLIhOL-t23VWt9nKxS0NK9c0me92Ycg-6pK6eM5OKtdEuviZU_Z6f_cyX_Dl88Pj_HbJvUQzcAkkc9RaKqAV5koL0k4r430JrtKkPSDJmfKl9qossCyMUtJXM5JCeWnklF2NuX3o3ncUB7tJX7TppBUmFSo0gEgqMap86GIMVNk-1G8uHCyC_YJjRzg2wbHfcOw-meRoikncrin8Rf_j-gTWv2bl</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Fu, Xubo</creator><creator>Zhang, Kun</creator><creator>Wang, Changgang</creator><creator>Fan, Chao</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20201201</creationdate><title>Multiple player tracking in basketball court videos</title><author>Fu, Xubo ; Zhang, Kun ; Wang, Changgang ; Fan, Chao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-30e36188350eb16582e8a859ccd0af8e8c01e345cd8c5d71d79553cf4e325c393</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Accuracy</topic><topic>Basketball</topic><topic>Computer Graphics</topic><topic>Computer Science</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Image Processing and Computer Vision</topic><topic>Methods</topic><topic>Monitoring</topic><topic>Multimedia Information Systems</topic><topic>Multiple target tracking</topic><topic>Neural networks</topic><topic>Pattern Recognition</topic><topic>Pedestrians</topic><topic>Performance evaluation</topic><topic>Players</topic><topic>Sensors</topic><topic>Signal,Image and Speech Processing</topic><topic>Special Issue Paper</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fu, Xubo</creatorcontrib><creatorcontrib>Zhang, Kun</creatorcontrib><creatorcontrib>Wang, Changgang</creatorcontrib><creatorcontrib>Fan, Chao</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Journal of real-time image processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fu, Xubo</au><au>Zhang, Kun</au><au>Wang, Changgang</au><au>Fan, Chao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multiple player tracking in basketball court videos</atitle><jtitle>Journal of real-time image processing</jtitle><stitle>J Real-Time Image Proc</stitle><date>2020-12-01</date><risdate>2020</risdate><volume>17</volume><issue>6</issue><spage>1811</spage><epage>1828</epage><pages>1811-1828</pages><issn>1861-8200</issn><eissn>1861-8219</eissn><abstract>To build a smart basketball court, one basic task is to track players with the aid of the basketball court monitoring. This task can be regarded as a special case of the multiple object tracking (MOT) problem. But different from it, the task puts request to the existing MOT methods with both good accuracy and operational efficiency (toward real time) in the new basketball scenario. To deal with this task, we make the following attempts: (1) Considering the differences between pedestrians and basketball players and the lack of corresponding dataset for basketball players tracking, we construct a new MOT dataset under the basketball court monitoring scene, to better evaluate MOT methods in our task and to help promote future research in the related task, (2) evaluating the performance of the several candidate MOT methods on the new dataset, and (3) proposing the issues to be further addressed in this specific scenario.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s11554-020-00968-x</doi><tpages>18</tpages></addata></record> |
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| language | eng |
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| subjects | Accuracy Basketball Computer Graphics Computer Science Datasets Deep learning Image Processing and Computer Vision Methods Monitoring Multimedia Information Systems Multiple target tracking Neural networks Pattern Recognition Pedestrians Performance evaluation Players Sensors Signal,Image and Speech Processing Special Issue Paper |
| title | Multiple player tracking in basketball court videos |
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