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Violence Detection using Deep Learning

International Journal of Science and Management Studies (IJSMS)
© 2024 by IJSMS Journal
Volume-7 Issue-1
Year of Publication : 2024
Authors : Mrs.P.V. Rajasuganya, Mr.Sabarivasan M.K, Mr.Tharun Eshwar S
DOI: 10.51386/25815946/ijsms-v7i1p114
Citation:
MLA Style: Mrs.P.V. Rajasuganya, Mr.Sabarivasan M.K, Mr.Tharun Eshwar S "Violence Detection using Deep Learning" International Journal of Science and Management Studies (IJSMS) V7.I1 (2024): 90-92.

APA Style: Mrs.P.V. Rajasuganya, Mr.Sabarivasan M.K, Mr.Tharun Eshwar S, Violence Detection using Deep Learning, International Journal of Science and Management Studies (IJSMS), v7(i1), 90-92.
Abstract:
In an era where technological advancements are reshaping various aspects of society, crime prevention and public safety have emerged as critical areas of concern.This project proposes a novel approach to crime analytics by harnessing the power of live footage and deep learning technologies.The invention of Crime Analytics Using Live Footage represents a groundbreaking advancement in the field of crime prevention and law enforcement. This technology harnesses the power of real-time video analysis, predictive analytics, and data-driven decision-making to proactively detect and prevent criminal activities, ensuring public safety and enhancing the efficiency of law enforcement efforts.The system's core functionality lies in its ability to analyze live video footage from a network of surveillance cameras, leveraging advanced computer vision algorithms and machine learning techniques. This real-time analysis enables the immediate identification of potential threats, suspicious behavior, and emerging criminal activities within monitored areas.
Keywords: Technological, Society, Crime Analytics, Live Footage, Real-Time Video, Computer, Algorithms, Network, Surveillance, Criminal Activities.
References:
[1] ViF-AD: "Violence Detection in Videos using Features from Adversarial Learning" - This paper presents a method that uses adversarial learning to detect violence in videos.
[2] ViP-VAE: "ViP-VAE: Violence-in-Pushing Video Analysis Engine" - This paper introduces a model for violence detection in pushing videos.
[3] Two-Stream CNN: You can look into two-stream Convolutional Neural Networks, which use both spatial and temporal information to detect violence in videos.
[4] I3D: The Inflated 3D ConvNet is a popular choice for action recognition in videos, which can be adapted for violence detection.
[5] 3D CNNs: 3D Convolutional Neural Networks are commonly used for video analysis tasks, including violence detection. Research papers and models using 3D CNNs are available.