The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Crowd monitoring using image processing
Abstract
The understanding of crowd behaviour in semi-confined spaces is an important part of the design of new pedestrian facilities, for major layout modifications to existing areas and for the daily management of sites-subject to crowd traffic. Conventional manual measurement techniques are not suitable for comprehensive data collection of patterns of site occupation and movement. Real-time monitoring is tedious and tiring, but safety-critical. This paper presents some image processing techniques being developed at King's College London which, using existing closed-circuit television systems, can support both data collection and on-line monitoring of crowds. The application of these methods could lead to a better understanding of crowd behaviour, improved design of the built environment and increased pedestrian safety.
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Citations
Cites
Natural movement: or, configuration and attraction in urban pedestrian movement
Hillier, B., Penn, A., Hanson, J., Grajewski, T., & Xu, J. (1993) · Environment and Planning B: Planning and Design · doi:10.1068/b200029 · Cited by 1,581 · cited 1×
“It is well known that crowd flow through airport and station concourses is related to the line-of-sight topology [1] of the structure.”
Background
Cited by
Fundamentals of Object Tracking
Challa, S., Morelande, M. R., Mušicki, D., & Evans, R. J. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511975837 · Cited by 416
Crowd Analysis Using Computer Vision Techniques
Silveira Jacques Junior, J., Musse, S., & Jung, C. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.937394 · Cited by 343
Crowd Counting Using Multiple Local Features
Ryan, D., Denman, S., Fookes, C., & Sridharan, S. (2009) · 2009 Digital Image Computing: Techniques and Applications · doi:10.1109/dicta.2009.22 · Cited by 309
The Geography of Taste: Analyzing Cell-Phone Mobility and Social Events
Calabrese, F., Pereira, F. C., Di Lorenzo, G., Liu, L., & Ratti, C. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-12654-3_2 · Cited by 278
Advances in Crowd Analysis for Urban Applications Through Urban Event Detection
Kaiser, M. S., Lwin, K. T., Mahmud, M., Hajializadeh, D., Chaipimonplin, T., Sarhan, A., & Hossain, M. A. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2771746 · Cited by 98
Data Fusion for Intelligent Crowd Monitoring and Management Systems: A Survey
Li, X., Yu, Q., Alzahrani, B., Barnawi, A., Alhindi, A., Alghazzawi, D., & Miao, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3060631 · Cited by 49
Multiple Cameras Using Real Time Object Tracking for Surveillance and Security System
Susheel Kumar, K., Prasad, S., Saroj, P. K., & Tripathi, R. C. (2010) · 2010 3rd International Conference on Emerging Trends in Engineering and Technology · doi:10.1109/icetet.2010.30 · Cited by 32
Metadata
- Authors
- Davies, A. C., Yin, J. H., & Velastin, S. A.
- Venue
- Electronics & Communication Engineering Journal
- Published
- 1995-02
- Volume
- 7
- Issue
- 1
- Pages
- 37-47
- ISSN
- 0954-0695 (ISSN-L)
- 2051-218X
- OpenAlex
- W2143043044
- Cited by
- 376
- References
- 1
- FWCI
- 1.44
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
