Robust Real-Time Violence Detection in Video Using CNN And LSTM

Reshma. S; Absa S.P; Riya S.R; Sreena V Wilson; Ms. R. Sajitha

Robust Real-Time Violence Detection in Video Using CNN And LSTM

Authors: Reshma. S, Absa S.P, Riya S.R, Sreena V Wilson, Ms. R. Sajitha

Unique Paper ID: 163048
Volume: 0
Issue: no
PageNo: 256-260

Keywords: No Keywords Found

Abstract:
A major part of law enforcement and public safety is the detection of violent events in surveillance systems. The speed, accuracy, and adaptability of violence event detectors across a range of video sources in various forms provide measures for measuring their success. A number of studies focused on speed, accuracy, or both when detecting violence, but they ignored to account for adaptability across various types of video sources. In this paper, we suggested a deep-learning based real-time violence detector. CNN serves as a geometric feature extractor in the proposed framework, while LSTM is used as a time-based relation learning technique with a focus on the three factors (accuracy, speed, and overall flexibility). The advised model reached 98%.

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ISSN: 2349-6002
Volume: 0
Issue: no
PageNo: 256-260

Robust Real-Time Violence Detection in Video Using CNN And LSTM

Available:https://ijirt.org/Article?manuscript=163048

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