{"title":"人群计数模型的预测性能和计算需求评价","authors":"","doi":"10.28919/cmbn/8097","DOIUrl":null,"url":null,"abstract":"With the increasing of human population and the development of technology, crowd counting models are needed to estimate people in certain areas. This research paper compares the prediction performance and computational requirement of four state of the art crowd counting models: M-SFAnet, DM-Count, Context-Aware Crowd Counting (ECAN), and Supervised Spatial Divide-and-Conquer (SS-DCNet). The evaluations were performed to find the most high-performance model in term of prediction performance and computational requirement. The computational requirement is being compared and considered because of the development of Internet of Things devices, crowd counting models that have good prediction performance and low computational requirements can be implemented in low-compute devices. We evaluated the models on four different datasets. From the evaluation we found that SS-DCNet approach achieved the most favorable results.","PeriodicalId":44079,"journal":{"name":"Communications in Mathematical Biology and Neuroscience","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluation of crowd counting models in term of prediction performance and computational requirement\",\"authors\":\"\",\"doi\":\"10.28919/cmbn/8097\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the increasing of human population and the development of technology, crowd counting models are needed to estimate people in certain areas. This research paper compares the prediction performance and computational requirement of four state of the art crowd counting models: M-SFAnet, DM-Count, Context-Aware Crowd Counting (ECAN), and Supervised Spatial Divide-and-Conquer (SS-DCNet). The evaluations were performed to find the most high-performance model in term of prediction performance and computational requirement. The computational requirement is being compared and considered because of the development of Internet of Things devices, crowd counting models that have good prediction performance and low computational requirements can be implemented in low-compute devices. We evaluated the models on four different datasets. From the evaluation we found that SS-DCNet approach achieved the most favorable results.\",\"PeriodicalId\":44079,\"journal\":{\"name\":\"Communications in Mathematical Biology and Neuroscience\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Communications in Mathematical Biology and Neuroscience\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.28919/cmbn/8097\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications in Mathematical Biology and Neuroscience","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.28919/cmbn/8097","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Evaluation of crowd counting models in term of prediction performance and computational requirement
With the increasing of human population and the development of technology, crowd counting models are needed to estimate people in certain areas. This research paper compares the prediction performance and computational requirement of four state of the art crowd counting models: M-SFAnet, DM-Count, Context-Aware Crowd Counting (ECAN), and Supervised Spatial Divide-and-Conquer (SS-DCNet). The evaluations were performed to find the most high-performance model in term of prediction performance and computational requirement. The computational requirement is being compared and considered because of the development of Internet of Things devices, crowd counting models that have good prediction performance and low computational requirements can be implemented in low-compute devices. We evaluated the models on four different datasets. From the evaluation we found that SS-DCNet approach achieved the most favorable results.
期刊介绍:
Communications in Mathematical Biology and Neuroscience (CMBN) is a peer-reviewed open access international journal, which is aimed to provide a publication forum for important research in all aspects of mathematical biology and neuroscience. This journal will accept high quality articles containing original research results and survey articles of exceptional merit.