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West Ashley Library
9 a.m. - 7 p.m.
Phone: (843) 766-6635
Wando Mount Pleasant Library
9 a.m. - 8 p.m.
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Village Library
9 a.m. - 6 p.m.
Phone: (843) 884-9741
St. Paul's/Hollywood Library
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Otranto Road Library
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Edisto Island Library
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Crowd detection and analysis for surveillance videos using deep learning.
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- Author(s): Sheikh, Aiman; Itkelwar, Rudra; Maskati, Amin; Malik, Deepika; Mohbansi, Angel; Khurana, Khusboo
- Source:
AIP Conference Proceedings; 2022, Vol. 2424 Issue 1, p1-8, 8p- Subject Terms:
- Source:
- Additional Information
- Abstract: Due to the reduced costs, the availability of surveillance systems has increased many folds. Surveillance videos have proved to be very important for crime investigation. The recent technological advancements have made it possible to analyze the video to extract valuable information from it. Mass or crowd gatherings can be seen at a lot of places like airports, sports stadiums, various religious, educational, and entertainment-related events, etc. Video surveillance at such crowded places can prove to be very helpful in crowd management. It is very important to identify the presence of a crowd and detect the number of people in the gathering. This can prove very useful for the detection of sudden troupe build-up, to avoid riots. Moreover, it can also be very useful in the Covid-19 pandemic situation to avoid people gathering at a place. In this paper a framework for crowd detection is presented. Presence of crowd is found by counting unique people and then performing crowd analysis. Crowd analysis is performed by detecting the gender and age of people in the crowd. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of AIP Conference Proceedings is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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