{"title":"有损无线网络中的视频源识别","authors":"Shaxun Chen, A. Pande, K. Zeng, P. Mohapatra","doi":"10.1109/INFCOM.2013.6566766","DOIUrl":null,"url":null,"abstract":"Video source identification is very important in validating video evidence, tracking down video piracy crimes and regulating individual video sources. With the prevalence of wireless communication, wireless video cameras continue to replace their wired counterparts in security/surveillance systems and tactical networks. However, wirelessly streamed videos usually suffer from blocking and blurring due to inevitable packet loss in wireless transmissions. The existing source identification methods experience significant performance degradation or even fail to work when identifying videos with blocking and blurring. In this paper, we propose a method which is effective and efficient in identifying such wirelessly streamed videos. In addition, we also propose to incorporate wireless channel signatures and selective frame processing into source identification, which significantly improve the identification speed.","PeriodicalId":206346,"journal":{"name":"2013 Proceedings IEEE INFOCOM","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Video source identification in lossy wireless networks\",\"authors\":\"Shaxun Chen, A. Pande, K. Zeng, P. Mohapatra\",\"doi\":\"10.1109/INFCOM.2013.6566766\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Video source identification is very important in validating video evidence, tracking down video piracy crimes and regulating individual video sources. With the prevalence of wireless communication, wireless video cameras continue to replace their wired counterparts in security/surveillance systems and tactical networks. However, wirelessly streamed videos usually suffer from blocking and blurring due to inevitable packet loss in wireless transmissions. The existing source identification methods experience significant performance degradation or even fail to work when identifying videos with blocking and blurring. In this paper, we propose a method which is effective and efficient in identifying such wirelessly streamed videos. In addition, we also propose to incorporate wireless channel signatures and selective frame processing into source identification, which significantly improve the identification speed.\",\"PeriodicalId\":206346,\"journal\":{\"name\":\"2013 Proceedings IEEE INFOCOM\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-04-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 Proceedings IEEE INFOCOM\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INFCOM.2013.6566766\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Proceedings IEEE INFOCOM","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INFCOM.2013.6566766","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Video source identification in lossy wireless networks
Video source identification is very important in validating video evidence, tracking down video piracy crimes and regulating individual video sources. With the prevalence of wireless communication, wireless video cameras continue to replace their wired counterparts in security/surveillance systems and tactical networks. However, wirelessly streamed videos usually suffer from blocking and blurring due to inevitable packet loss in wireless transmissions. The existing source identification methods experience significant performance degradation or even fail to work when identifying videos with blocking and blurring. In this paper, we propose a method which is effective and efficient in identifying such wirelessly streamed videos. In addition, we also propose to incorporate wireless channel signatures and selective frame processing into source identification, which significantly improve the identification speed.