{"title":"从多传感器数据中自动生成个性化篮球视频摘要","authors":"Fan Chen, C. Vleeschouwer","doi":"10.1109/ICIP.2010.5652750","DOIUrl":null,"url":null,"abstract":"We propose a flexible framework for producing highly personalized basketball video summaries, by intergrating contextural information, narrative user preferences on story pattern, and general production principles. Starting from the multiple streams captured by a distributed set of fixed cameras, we study the implementation of autonomous viewpoint determination and automatic temporal segment selection, and also discuss the production of visually comfortable output, by applying smoothing process to viewpoint selection and by defining efficient benefit functions to evaluate various summary organization. The efficiency of our framework is demonstrated by experimental results.","PeriodicalId":228308,"journal":{"name":"2010 IEEE International Conference on Image Processing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Automatic production of personalized basketball video summaries from multi-sensored data\",\"authors\":\"Fan Chen, C. Vleeschouwer\",\"doi\":\"10.1109/ICIP.2010.5652750\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a flexible framework for producing highly personalized basketball video summaries, by intergrating contextural information, narrative user preferences on story pattern, and general production principles. Starting from the multiple streams captured by a distributed set of fixed cameras, we study the implementation of autonomous viewpoint determination and automatic temporal segment selection, and also discuss the production of visually comfortable output, by applying smoothing process to viewpoint selection and by defining efficient benefit functions to evaluate various summary organization. The efficiency of our framework is demonstrated by experimental results.\",\"PeriodicalId\":228308,\"journal\":{\"name\":\"2010 IEEE International Conference on Image Processing\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE International Conference on Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIP.2010.5652750\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.2010.5652750","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic production of personalized basketball video summaries from multi-sensored data
We propose a flexible framework for producing highly personalized basketball video summaries, by intergrating contextural information, narrative user preferences on story pattern, and general production principles. Starting from the multiple streams captured by a distributed set of fixed cameras, we study the implementation of autonomous viewpoint determination and automatic temporal segment selection, and also discuss the production of visually comfortable output, by applying smoothing process to viewpoint selection and by defining efficient benefit functions to evaluate various summary organization. The efficiency of our framework is demonstrated by experimental results.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
