{"title":"通过众包增强的自动化人群流量估计","authors":"Javier Ribera, Khalid Tahboub, E. Delp","doi":"10.1109/NAECON.2014.7045798","DOIUrl":null,"url":null,"abstract":"Video surveillance systems that contain a large number of cameras makes the continuous monitoring of the video feeds nearly an impossible task. If the information from these many cameras is to be exploited automatic video analytic techniques must be developed. In this paper, we present improvements to a previously developed crowd flow estimation method. We use crowdsourcing techniques to enhance the performance. An experimental evaluation is conducted using publicly available datasets.","PeriodicalId":318539,"journal":{"name":"NAECON 2014 - IEEE National Aerospace and Electronics Conference","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Automated crowd flow estimation enhanced by crowdsourcing\",\"authors\":\"Javier Ribera, Khalid Tahboub, E. Delp\",\"doi\":\"10.1109/NAECON.2014.7045798\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Video surveillance systems that contain a large number of cameras makes the continuous monitoring of the video feeds nearly an impossible task. If the information from these many cameras is to be exploited automatic video analytic techniques must be developed. In this paper, we present improvements to a previously developed crowd flow estimation method. We use crowdsourcing techniques to enhance the performance. An experimental evaluation is conducted using publicly available datasets.\",\"PeriodicalId\":318539,\"journal\":{\"name\":\"NAECON 2014 - IEEE National Aerospace and Electronics Conference\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"NAECON 2014 - IEEE National Aerospace and Electronics Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NAECON.2014.7045798\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"NAECON 2014 - IEEE National Aerospace and Electronics Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAECON.2014.7045798","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automated crowd flow estimation enhanced by crowdsourcing
Video surveillance systems that contain a large number of cameras makes the continuous monitoring of the video feeds nearly an impossible task. If the information from these many cameras is to be exploited automatic video analytic techniques must be developed. In this paper, we present improvements to a previously developed crowd flow estimation method. We use crowdsourcing techniques to enhance the performance. An experimental evaluation is conducted using publicly available datasets.
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