{"title":"Automatic classification of tennis video for high-level content-based retrieval","authors":"G. Sudhir, J. C. Lee, Anil K. Jain","doi":"10.1109/CAIVD.1998.646036","DOIUrl":null,"url":null,"abstract":"We present our techniques and results on automatic analysis of tennis video to facilitate content-based retrieval. Our approach is based on the generation of an image model for the tennis court-lines. We derive this model by using the knowledge about dimensions and connectivity (form) of a tennis court and typical camera geometry used when capturing a tennis video. We use this model to develop: a court line detection algorithm; and a robust player tracking algorithm to track the tennis players over the image sequence. We also present a color-based algorithm to select tennis court clips from an input raw footage of tennis video. Automatically extracted tennis court lines and the players' location information are analyzed in a high-level reasoning module and related to useful high-level tennis play events. Results on real tennis video data are presented demonstrating the validity and performance of the approach.","PeriodicalId":360087,"journal":{"name":"Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"254","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAIVD.1998.646036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 254
Abstract
We present our techniques and results on automatic analysis of tennis video to facilitate content-based retrieval. Our approach is based on the generation of an image model for the tennis court-lines. We derive this model by using the knowledge about dimensions and connectivity (form) of a tennis court and typical camera geometry used when capturing a tennis video. We use this model to develop: a court line detection algorithm; and a robust player tracking algorithm to track the tennis players over the image sequence. We also present a color-based algorithm to select tennis court clips from an input raw footage of tennis video. Automatically extracted tennis court lines and the players' location information are analyzed in a high-level reasoning module and related to useful high-level tennis play events. Results on real tennis video data are presented demonstrating the validity and performance of the approach.