{"title":"基于视频监控数据的交通强度评估方法","authors":"O. Lebedeva, Ekaterina Savvateeva","doi":"10.36629/2686-7788-2023-1-149-153","DOIUrl":null,"url":null,"abstract":"The paper presents an approach to intelligent transport systems based on advanced machine learning algorithms, the purpose of which is to estimate the traffic flow on the road network. Data is obtained from traffic cameras located in a limited number of places. The approach includes two main algorithms. The first is a probabilistic algorithm for counting vehicles from low-quality images, which belongs to the category of unsupervised learning","PeriodicalId":361424,"journal":{"name":"Scientific Papers Collection of the Angarsk State Technical University","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"METHODOLOGY FOR ASSESSING TRAFFIC INTENSITY ON THE BASIS OF VIDEO SURVEILLANCE DATA\",\"authors\":\"O. Lebedeva, Ekaterina Savvateeva\",\"doi\":\"10.36629/2686-7788-2023-1-149-153\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper presents an approach to intelligent transport systems based on advanced machine learning algorithms, the purpose of which is to estimate the traffic flow on the road network. Data is obtained from traffic cameras located in a limited number of places. The approach includes two main algorithms. The first is a probabilistic algorithm for counting vehicles from low-quality images, which belongs to the category of unsupervised learning\",\"PeriodicalId\":361424,\"journal\":{\"name\":\"Scientific Papers Collection of the Angarsk State Technical University\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Papers Collection of the Angarsk State Technical University\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.36629/2686-7788-2023-1-149-153\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Papers Collection of the Angarsk State Technical University","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36629/2686-7788-2023-1-149-153","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
METHODOLOGY FOR ASSESSING TRAFFIC INTENSITY ON THE BASIS OF VIDEO SURVEILLANCE DATA
The paper presents an approach to intelligent transport systems based on advanced machine learning algorithms, the purpose of which is to estimate the traffic flow on the road network. Data is obtained from traffic cameras located in a limited number of places. The approach includes two main algorithms. The first is a probabilistic algorithm for counting vehicles from low-quality images, which belongs to the category of unsupervised learning
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