{"title":"基于复杂卷积神经网络的人群计数","authors":"Marcin Matlacz, G. Sarwas","doi":"10.23919/SPA.2018.8563383","DOIUrl":null,"url":null,"abstract":"This paper is focused on the problem of counting people in crowd. For solving this issue a complex valued convolutional neural network has been proposed. The network training and evaluation have been processed using datasets ShanghaiTech and UCF_CC_50, respectively. Achieved results have been compared with other algorithms for crowd counting based on the deep neural network architecture, mainly “CrowdNet” algorithm. Proposed model achieved better results than equivalent real-valued model.","PeriodicalId":265587,"journal":{"name":"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Crowd counting using complex convolutional neural network\",\"authors\":\"Marcin Matlacz, G. Sarwas\",\"doi\":\"10.23919/SPA.2018.8563383\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper is focused on the problem of counting people in crowd. For solving this issue a complex valued convolutional neural network has been proposed. The network training and evaluation have been processed using datasets ShanghaiTech and UCF_CC_50, respectively. Achieved results have been compared with other algorithms for crowd counting based on the deep neural network architecture, mainly “CrowdNet” algorithm. Proposed model achieved better results than equivalent real-valued model.\",\"PeriodicalId\":265587,\"journal\":{\"name\":\"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"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.2018.8563383\",\"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.2018.8563383","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Crowd counting using complex convolutional neural network
This paper is focused on the problem of counting people in crowd. For solving this issue a complex valued convolutional neural network has been proposed. The network training and evaluation have been processed using datasets ShanghaiTech and UCF_CC_50, respectively. Achieved results have been compared with other algorithms for crowd counting based on the deep neural network architecture, mainly “CrowdNet” algorithm. Proposed model achieved better results than equivalent real-valued model.