{"title":"时变人群密度分布可视化的自适应方法","authors":"Marianna Parzych, T. Marciniak, A. Dabrowski","doi":"10.23919/SPA.2018.8563391","DOIUrl":null,"url":null,"abstract":"The paper presents an analysis of visualization methods of crowd density visualization. Generated density maps take into account changes in time. Three methods have been implemented and tested. The first one uses motion detection based on the background subtraction. The second one is based on BLOBs (binary large objects) analysis. The third method uses interest points ie. points on the image that can be used by the object track the movement. The tests were performed using the PETS2009 video sequence database. The obtained maps were evaluated and the time consumptions were estimated.","PeriodicalId":265587,"journal":{"name":"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive methods of time-dependent crowd density distribution visualization\",\"authors\":\"Marianna Parzych, T. Marciniak, A. Dabrowski\",\"doi\":\"10.23919/SPA.2018.8563391\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper presents an analysis of visualization methods of crowd density visualization. Generated density maps take into account changes in time. Three methods have been implemented and tested. The first one uses motion detection based on the background subtraction. The second one is based on BLOBs (binary large objects) analysis. The third method uses interest points ie. points on the image that can be used by the object track the movement. The tests were performed using the PETS2009 video sequence database. The obtained maps were evaluated and the time consumptions were estimated.\",\"PeriodicalId\":265587,\"journal\":{\"name\":\"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/SPA.2018.8563391\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/SPA.2018.8563391","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive methods of time-dependent crowd density distribution visualization
The paper presents an analysis of visualization methods of crowd density visualization. Generated density maps take into account changes in time. Three methods have been implemented and tested. The first one uses motion detection based on the background subtraction. The second one is based on BLOBs (binary large objects) analysis. The third method uses interest points ie. points on the image that can be used by the object track the movement. The tests were performed using the PETS2009 video sequence database. The obtained maps were evaluated and the time consumptions were estimated.
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