A fully integrated violence detection system using CNN and LSTM

Sarthak Sharma, B. Sudharsan, Saamaja Naraharisetti, Vimarsh Trehan, Kayalvizhi Jayavel


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.


deep learning; LSTM; mobile application; smart cities; transfer learning; violence detection;

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DOI: http://doi.org/10.11591/ijece.v11i4.pp3374-3380

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).