A Crowd Counting Framework Combining with Crowd Location
In the past ten years, crowd detection and counting have been applied in many fields such as station crowd statistics, urban safety prevention, and people flow statistics. However, obtaining accurate positions and improving the performance of crowd counting in dense scenes still face challenges, and...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/6664281 |
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| _version_ | 1850166837984100352 |
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| author | Jin Zhang Sheng Chen Sen Tian Wenan Gong Guoshan Cai Ying Wang |
| author_facet | Jin Zhang Sheng Chen Sen Tian Wenan Gong Guoshan Cai Ying Wang |
| author_sort | Jin Zhang |
| collection | DOAJ |
| description | In the past ten years, crowd detection and counting have been applied in many fields such as station crowd statistics, urban safety prevention, and people flow statistics. However, obtaining accurate positions and improving the performance of crowd counting in dense scenes still face challenges, and it is worthwhile devoting much effort to this. In this paper, a new framework is proposed to resolve the problem. The proposed framework includes two parts. The first part is a fully convolutional neural network (CNN) consisting of backend and upsampling. In the first part, backend uses the residual network (ResNet) to encode the features of the input picture, and upsampling uses the deconvolution layer to decode the feature information. The first part processes the input image, and the processed image is input to the second part. The second part is a peak confidence map (PCM), which is proposed based on an improvement over the density map (DM). Compared with DM, PCM can not only solve the problem of crowd counting but also accurately predict the location of the person. The experimental results on several datasets (Beijing-BRT, Mall, Shanghai Tech, and UCF_CC_50 datasets) show that the proposed framework can achieve higher crowd counting performance in dense scenarios and can accurately predict the location of crowds. |
| format | Article |
| id | doaj-art-de157aa6e19c4f1fa46d7a7e97e364df |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-de157aa6e19c4f1fa46d7a7e97e364df2025-08-20T02:21:20ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/66642816664281A Crowd Counting Framework Combining with Crowd LocationJin Zhang0Sheng Chen1Sen Tian2Wenan Gong3Guoshan Cai4Ying Wang5College of Informatica Science and Engineering, Hunan Normal University, Changsha 410081, ChinaCollege of Informatica Science and Engineering, Hunan Normal University, Changsha 410081, ChinaCollege of Mathematics and Statistics, Hunan Normal University, Changsha 410081, ChinaChangsha Transportation Information Center, Changsha 410016, ChinaChangsha Tianxia Yida Information Technology Co., Ltd., Changsha 410221, ChinaSchool of Humanities and Management, Hunan University of Chinese Medicine, Changsha 410208, ChinaIn the past ten years, crowd detection and counting have been applied in many fields such as station crowd statistics, urban safety prevention, and people flow statistics. However, obtaining accurate positions and improving the performance of crowd counting in dense scenes still face challenges, and it is worthwhile devoting much effort to this. In this paper, a new framework is proposed to resolve the problem. The proposed framework includes two parts. The first part is a fully convolutional neural network (CNN) consisting of backend and upsampling. In the first part, backend uses the residual network (ResNet) to encode the features of the input picture, and upsampling uses the deconvolution layer to decode the feature information. The first part processes the input image, and the processed image is input to the second part. The second part is a peak confidence map (PCM), which is proposed based on an improvement over the density map (DM). Compared with DM, PCM can not only solve the problem of crowd counting but also accurately predict the location of the person. The experimental results on several datasets (Beijing-BRT, Mall, Shanghai Tech, and UCF_CC_50 datasets) show that the proposed framework can achieve higher crowd counting performance in dense scenarios and can accurately predict the location of crowds.http://dx.doi.org/10.1155/2021/6664281 |
| spellingShingle | Jin Zhang Sheng Chen Sen Tian Wenan Gong Guoshan Cai Ying Wang A Crowd Counting Framework Combining with Crowd Location Journal of Advanced Transportation |
| title | A Crowd Counting Framework Combining with Crowd Location |
| title_full | A Crowd Counting Framework Combining with Crowd Location |
| title_fullStr | A Crowd Counting Framework Combining with Crowd Location |
| title_full_unstemmed | A Crowd Counting Framework Combining with Crowd Location |
| title_short | A Crowd Counting Framework Combining with Crowd Location |
| title_sort | crowd counting framework combining with crowd location |
| url | http://dx.doi.org/10.1155/2021/6664281 |
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