Framework for timely perception of spatiotemporal crowd congestion risk in public spaces based on video pedestrian tracking and geographic mapping
As urbanization intensifies, congestion and safety concerns in various public venues have garnered significant societal attention, presenting substantial challenges for public safety management and emergency response. To address this issue, developing a technological framework capable of automatic p...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2025.2480416 |
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| author | Shaojun Liu Ling Zhang Junlian Ge Weitao Li Yi Long |
| author_facet | Shaojun Liu Ling Zhang Junlian Ge Weitao Li Yi Long |
| author_sort | Shaojun Liu |
| collection | DOAJ |
| description | As urbanization intensifies, congestion and safety concerns in various public venues have garnered significant societal attention, presenting substantial challenges for public safety management and emergency response. To address this issue, developing a technological framework capable of automatic perception, analysis, and early warning of crowd dynamics is urgently needed. Current technologies face hindrances such as low data precision, limited generalization across diverse scenarios, incomplete crowd coverage, and sluggish responsiveness. The popularity of sensor networks and the rapid development of computer vision technology have made it possible to collect and perceive spatial and temporal information ubiquitously, creating valuable opportunities for the rapid analysis of geographic phenomena and timely detection of potential problems. This study drew on the concepts of social comfort distance and spatial carrying capacity by employing high-coverage surveillance video streams as the data source. We introduced a framework for crowd activity perception and scene-adaptive congestion risk assessment based on a multi-object tracking model. This framework established a mutual mapping between the image and geographic spaces, facilitating precise spatiotemporal identification and grading of congestion. The validity of the method was demonstrated across various scenes in a tourist area through a comprehensive analysis of its perception accuracy and efficiency. The flexible and modular architecture of the proposed technology paves the way for its broader application and offers an effective solution for refined crowd management in public spaces. |
| format | Article |
| id | doaj-art-8bba6c298d53422e85eb9211b2e3435d |
| institution | DOAJ |
| issn | 1548-1603 1943-7226 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj-art-8bba6c298d53422e85eb9211b2e3435d2025-08-20T03:21:31ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262025-12-0162110.1080/15481603.2025.2480416Framework for timely perception of spatiotemporal crowd congestion risk in public spaces based on video pedestrian tracking and geographic mappingShaojun Liu0Ling Zhang1Junlian Ge2Weitao Li3Yi Long4School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, ChinaMinistry of Education, Key Lab of Virtual Geographic Environment (Nanjing Normal University), Nanjing, ChinaMinistry of Education, Key Lab of Virtual Geographic Environment (Nanjing Normal University), Nanjing, ChinaAnhui Province Key Laboratory of Physical Geographic Environment, Chuzhou University, Chuzhou, ChinaMinistry of Education, Key Lab of Virtual Geographic Environment (Nanjing Normal University), Nanjing, ChinaAs urbanization intensifies, congestion and safety concerns in various public venues have garnered significant societal attention, presenting substantial challenges for public safety management and emergency response. To address this issue, developing a technological framework capable of automatic perception, analysis, and early warning of crowd dynamics is urgently needed. Current technologies face hindrances such as low data precision, limited generalization across diverse scenarios, incomplete crowd coverage, and sluggish responsiveness. The popularity of sensor networks and the rapid development of computer vision technology have made it possible to collect and perceive spatial and temporal information ubiquitously, creating valuable opportunities for the rapid analysis of geographic phenomena and timely detection of potential problems. This study drew on the concepts of social comfort distance and spatial carrying capacity by employing high-coverage surveillance video streams as the data source. We introduced a framework for crowd activity perception and scene-adaptive congestion risk assessment based on a multi-object tracking model. This framework established a mutual mapping between the image and geographic spaces, facilitating precise spatiotemporal identification and grading of congestion. The validity of the method was demonstrated across various scenes in a tourist area through a comprehensive analysis of its perception accuracy and efficiency. The flexible and modular architecture of the proposed technology paves the way for its broader application and offers an effective solution for refined crowd management in public spaces.https://www.tandfonline.com/doi/10.1080/15481603.2025.2480416Crowd congestionrisk evaluationubiquitous perceptionmulti-object trackingtourist flow management |
| spellingShingle | Shaojun Liu Ling Zhang Junlian Ge Weitao Li Yi Long Framework for timely perception of spatiotemporal crowd congestion risk in public spaces based on video pedestrian tracking and geographic mapping GIScience & Remote Sensing Crowd congestion risk evaluation ubiquitous perception multi-object tracking tourist flow management |
| title | Framework for timely perception of spatiotemporal crowd congestion risk in public spaces based on video pedestrian tracking and geographic mapping |
| title_full | Framework for timely perception of spatiotemporal crowd congestion risk in public spaces based on video pedestrian tracking and geographic mapping |
| title_fullStr | Framework for timely perception of spatiotemporal crowd congestion risk in public spaces based on video pedestrian tracking and geographic mapping |
| title_full_unstemmed | Framework for timely perception of spatiotemporal crowd congestion risk in public spaces based on video pedestrian tracking and geographic mapping |
| title_short | Framework for timely perception of spatiotemporal crowd congestion risk in public spaces based on video pedestrian tracking and geographic mapping |
| title_sort | framework for timely perception of spatiotemporal crowd congestion risk in public spaces based on video pedestrian tracking and geographic mapping |
| topic | Crowd congestion risk evaluation ubiquitous perception multi-object tracking tourist flow management |
| url | https://www.tandfonline.com/doi/10.1080/15481603.2025.2480416 |
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