In modern society, dense crowd detection technology is particularly important due to the frequent occurrence of crowd scenes such as stations, shopping malls, and event sites, which are often accompanied by safety ris...In modern society, dense crowd detection technology is particularly important due to the frequent occurrence of crowd scenes such as stations, shopping malls, and event sites, which are often accompanied by safety risks, like stampede accidents. Although many studies have made progress in estimating population density, the ability to accurately identify dense areas in multi-scale scenarios still needs to be improved. To solve this problem, this paper proposed an improved multi-scale dense crowd detection method based on YOLOv5 and improved the DBSCAN clustering algorithm to identify densely crowded areas. Experiments show that the improved multi-scale dense crowd detection method can identify target crowds at multiple scales, and the accuracy of its detection results is around 70%. In addition, by calculating the crowd density under the same scale conditions and visualising the dense areas, we were able to solve the problem of dividing the crowded areas and visualise the dense areas more accurately. These improvements enhanced the applicability and reliability of the model in practical applications and provided strong technical support for security monitoring and management.展开更多
Aiming at the problem of inaccurate crowd counting and location in dense scenes,a dynamic region-sensing crowd counting and location method based on high-resolution fusion was proposed.Firstly,U-HRNet was used as the ...Aiming at the problem of inaccurate crowd counting and location in dense scenes,a dynamic region-sensing crowd counting and location method based on high-resolution fusion was proposed.Firstly,U-HRNet was used as the main backbone to extract highresolution features of the population and enhance the ability of feature extraction with different resolutions.Then,the dynamic regional awareness attention module was designed to make full use of the global and local feature information,refine the differentiated learning of target feature and background feature,reduce the interference of background feature,and improve the positioning performance of the model.Finally,the predicted threshold map and confidence map were input into the binarization module to output the prediction and counting results of the crowd independent individual target.Experimental results showed that the proposed method achieved good performance of counting and positioning in different scenarios.展开更多
文摘In modern society, dense crowd detection technology is particularly important due to the frequent occurrence of crowd scenes such as stations, shopping malls, and event sites, which are often accompanied by safety risks, like stampede accidents. Although many studies have made progress in estimating population density, the ability to accurately identify dense areas in multi-scale scenarios still needs to be improved. To solve this problem, this paper proposed an improved multi-scale dense crowd detection method based on YOLOv5 and improved the DBSCAN clustering algorithm to identify densely crowded areas. Experiments show that the improved multi-scale dense crowd detection method can identify target crowds at multiple scales, and the accuracy of its detection results is around 70%. In addition, by calculating the crowd density under the same scale conditions and visualising the dense areas, we were able to solve the problem of dividing the crowded areas and visualise the dense areas more accurately. These improvements enhanced the applicability and reliability of the model in practical applications and provided strong technical support for security monitoring and management.
基金supported by MOE(Ministry of Education in China)Project of Humanities and Social Sciences(No.19YJC760012)Key Research and Development Project of Lanzhou Jiaotong University(No.ZDYF2304).
文摘Aiming at the problem of inaccurate crowd counting and location in dense scenes,a dynamic region-sensing crowd counting and location method based on high-resolution fusion was proposed.Firstly,U-HRNet was used as the main backbone to extract highresolution features of the population and enhance the ability of feature extraction with different resolutions.Then,the dynamic regional awareness attention module was designed to make full use of the global and local feature information,refine the differentiated learning of target feature and background feature,reduce the interference of background feature,and improve the positioning performance of the model.Finally,the predicted threshold map and confidence map were input into the binarization module to output the prediction and counting results of the crowd independent individual target.Experimental results showed that the proposed method achieved good performance of counting and positioning in different scenarios.
