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A fully integrated violence detection system using CNN and LSTM

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 11 No. 4 (2021)

DOI
10.11591/ijece.v11i4.pp3374-3380
ISSN
2722-2578, 2088-8708
Published
2021-08-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Recently, the number of violence-related cases in places such as remote roads, pathways, shopping malls, elevators, sports stadiums, and liquor shops, has increased drastically which are unfortunately discovered only after it’s too late. The aim is to create a complete system that can perform real-time video analysis which will help recognize the presence of any violent activities and notify the same to the concerned authority, such as the police department of the corresponding area. Using the deep learning networks CNN and LSTM along with a well-defined system architecture, we have achieved an efficient solution that can be used for real-time analysis of video footage so that the concerned authority can monitor the situation through a mobile application that can notify about an occurrence of a violent event immediately.

Collaboration network of the authors

8 authors · 14 co-authorship links

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  • SRM Institute of Science and Technology
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Akella S. Narasimha RajuSRM Institute of Science and Technology1
Tulasi RajalakshmiSRM Institute of Science and Technology1
B. Cynthia SherinSRM Institute of Science and Technology1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 1 Nov 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>A fully integrated violence detection system using CNN and LSTM</dc:title><dc:creator>Sharma, Sarthak</dc:creator><dc:creator>Sudharsan, B.</dc:creator><dc:creator>Naraharisetti, Saamaja</dc:creator><dc:creator>Trehan, Vimarsh</dc:creator><dc:creator>Jayavel, Kayalvizhi</dc:creator><dc:description>&lt;jats:p&gt;Recently, the number of violence-related cases in places such as remote roads, pathways, shopping malls, elevators, sports stadiums, and liquor shops, has increased drastically which are unfortunately discovered only after it’s too late. The aim is to create a complete system that can perform real-time video analysis which will help recognize the presence of any violent activities and notify the same to the concerned authority, such as the police department of the corresponding area. Using the deep learning networks CNN and LSTM along with a well-defined system architecture, we have achieved an efficient solution that can be used for real-time analysis of video footage so that the concerned authority can monitor the situation through a mobile application that can notify about an occurrence of a violent event immediately.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2021-08-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v11i4.pp3374-3380</dc:identifier><dc:identifier>10.11591/ijece.v11i4.pp3374-3380</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 11 No 4 (2021); 3374</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/23527/14955</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>