High-frequency crowd insights for public safety and congestion control
We present results from several projects aimed at enabling the real-time understanding of crowds and their behaviour in the built environment. We make use of CCTV video cameras that are ubiquitous throughout the developed and developing world and as such are able to play the role of a reliable sensi...
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| creator | Nandakumar, Karthik Blandin, Sebastien Wynter, Laura |
| description | We present results from several projects aimed at enabling the real-time
understanding of crowds and their behaviour in the built environment. We make
use of CCTV video cameras that are ubiquitous throughout the developed and
developing world and as such are able to play the role of a reliable sensing
mechanism. We outline the novel methods developed for our crowd insights
engine, and illustrate examples of its use in different contexts in the urban
landscape. Applications of the technology range from maintaining security in
public spaces to quantifying the adequacy of public transport level of service. |
| doi_str_mv | 10.48550/arxiv.1904.10180 |
| format | Article |
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understanding of crowds and their behaviour in the built environment. We make
use of CCTV video cameras that are ubiquitous throughout the developed and
developing world and as such are able to play the role of a reliable sensing
mechanism. We outline the novel methods developed for our crowd insights
engine, and illustrate examples of its use in different contexts in the urban
landscape. Applications of the technology range from maintaining security in
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understanding of crowds and their behaviour in the built environment. We make
use of CCTV video cameras that are ubiquitous throughout the developed and
developing world and as such are able to play the role of a reliable sensing
mechanism. We outline the novel methods developed for our crowd insights
engine, and illustrate examples of its use in different contexts in the urban
landscape. Applications of the technology range from maintaining security in
public spaces to quantifying the adequacy of public transport level of service.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj7FOwzAYhL0woMIDMNUvkOA_tmNnRBWlSJVYuke287tYCnaxUyBvT1oYTne6k076CHkAVgstJXs0-Sd81dAxUQMDzW7JdheO75XP-HnG6GbqcvoeaIhlqadCfcr0dLZjcLQYj9NMTRyoS_GIZQopXuKU03hHbrwZC97_-4octs-Hza7av728bp72lWkVqww45bXminXKSm9khxYHLWwLYHkDyD060VhsVMuFYFaC4nJZrfaLOF-R9d_tFaQ_5fBh8txfgPorEP8FXFZGTg</recordid><startdate>20190423</startdate><enddate>20190423</enddate><creator>Nandakumar, Karthik</creator><creator>Blandin, Sebastien</creator><creator>Wynter, Laura</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20190423</creationdate><title>High-frequency crowd insights for public safety and congestion control</title><author>Nandakumar, Karthik ; Blandin, Sebastien ; Wynter, Laura</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a670-a1c7f8837097b5fa59ebed84b611b321e3fec42be2763440b51735b61b8f1b833</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Nandakumar, Karthik</creatorcontrib><creatorcontrib>Blandin, Sebastien</creatorcontrib><creatorcontrib>Wynter, Laura</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Nandakumar, Karthik</au><au>Blandin, Sebastien</au><au>Wynter, Laura</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>High-frequency crowd insights for public safety and congestion control</atitle><date>2019-04-23</date><risdate>2019</risdate><abstract>We present results from several projects aimed at enabling the real-time
understanding of crowds and their behaviour in the built environment. We make
use of CCTV video cameras that are ubiquitous throughout the developed and
developing world and as such are able to play the role of a reliable sensing
mechanism. We outline the novel methods developed for our crowd insights
engine, and illustrate examples of its use in different contexts in the urban
landscape. Applications of the technology range from maintaining security in
public spaces to quantifying the adequacy of public transport level of service.</abstract><doi>10.48550/arxiv.1904.10180</doi><oa>free_for_read</oa></addata></record> |
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| identifier | DOI: 10.48550/arxiv.1904.10180 |
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| subjects | Computer Science - Computer Vision and Pattern Recognition |
| title | High-frequency crowd insights for public safety and congestion control |
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