Violence Detection System using Convolution Neural Network

Sakthivinayagam, Goutham; Easawarakumar, Raveena; Arunachalam, Alagappan; Pandi, M

doi:10.14445/23488549/ijece-v6i2p102

Cited by 4 publications

(5 citation statements)

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“…The paper's accuracy rate is 82% [12]. This paper shows CNN networks have low computational cost [13]. The paper uses VGG19 as the base model followed by LSTM .…”

Section: Literature Reviewmentioning

confidence: 95%

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Real time Violence and Weapon detection and Alert System

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Patil,

Pattanshetti

2024

Preprint

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With the proliferation of surveillance systems and the increasing need for public safety, real-time violence detection has emerged as a crucial research area. This project presents a novel approach to real-time violence detection leveraging the power of deep learning through the integration of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory Networks (LSTM) and weapon detection using You Only Look Once algorithm. The proposed system not only accurately identifies violent activities and weapons in video streams but also promptly issues alerts for timely intervention. The proposed system aims to address the challenges associated with accurately and swiftly identifying violent activities and weapons in video streams, making it applicable to diverse settings such as public spaces. The CNN component of the model is designed to automatically extract relevant spatial features from video frames, enabling the network to discern patterns associated with violent actions. Simultaneously, the Bi-LSTM network is employed to capture temporal dependencies and dynamic sequences within the video stream, allowing the model to understand the evolution of events over time. The fusion of spatial and temporal information enhances the robustness and accuracy of violence detection, especially in complex scenarios where contextual information is crucial. To further augment the system's capabilities, we use the You Only Look Once (YOLO) for weapon detection. YOLO is a state-of-the-art object detection algorithm that operates in real-time with high accuracy. By incorporating YOLO into our system, we enhance its ability to not only detect violent activities but also identify the presence of weapons in the scene. This additional functionality adds a crucial layer of security and enables authorities to respond more effectively to potential threats. Overall, the combined approach of CNN, Bi-LSTM, and YOLO offers a comprehensive solution for real-time violence detection in video streams, with the added capability of weapon detection. This integrated system contributes to enhancing public safety and security in various settings, including public spaces, by enabling timely intervention and response to potential threats.

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“…The paper's accuracy rate is 82% [12]. This paper shows CNN networks have low computational cost [13]. The paper uses VGG19 as the base model followed by LSTM .…”

Section: Literature Reviewmentioning

confidence: 95%

“…A recent trend shows extensive use of deep learning methods as convolutional neural network [5] [6] and Long short term memory [7]. These deep methods are better fit for temporal feature extraction than the algorithms which are handcrafted.…”

Section: Literature Reviewmentioning

confidence: 99%

Real time Violence and Weapon detection and Alert System

More,

Patil,

Pattanshetti

2024

Preprint

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“…We use this technique to apply the Xception network to all 15 inputs to get feature maps with 2048 channels. These feature maps are then flattened to a 2D tensor of shape (15,100352) which is fed into the LSTM having 512 cells that try to learn time relations between 15-time steps. Finally, we take the 1D predictions from LSTM and feed it to a series of dense layers to get the output predictions.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Recent research work on fight and violence detection shows the extensive implementation of deep learning architectures such as convolutional neural networks (CNNs) [15], long short-term memory (LSTMs), and two stream CNNs [16]. These automatic methods perform much better than the hand-crafted algorithms used for spatio-temporal feature extraction.…”

Section: Related Workmentioning

confidence: 99%

A fully integrated violence detection system using CNN and LSTM

Sharma

Sudharsan

Naraharisetti

et al. 2021

IJECE

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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.

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“…Here, we have one input layer, one output layer, and multiple intermediate or hidden layers of computational elements, i.e., neurons. In Figure 3, X is the input layer, Y is the output layer, and all others are hidden layers; weights between different sets of layers can be different as activation functions [6].…”

Section: Fig 2 a Single Layer Neural Network Architecturementioning

confidence: 99%

Autonomous Anomaly Detection System for Crime Monitoring and Alert Generation

Kukade

Soner

Pandya

2023

JAMRIS

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Nowadays violence in society impacts more than any weapon used by people. Over the last few years, autonomous vehicles have been used to observe and recognize the abnormality in the behavior of people and classifying them as a crime or not. Detecting crime on live stream is a classification of any event in a crime or not a crime and generating alerts to designated authorities which in turn can take required actions and ensure the security assessment of the city. Such kind of effective techniques for live video stream processing in computer vision is the need of the hour. There are many techniques that can be used, while Long Short-Term Memory (LSTM) networks and OpenCV provide the most accurate prediction for this task. OpenCV is used for the task of object detection in computer vision which will take the input from either drone or any autonomous vehicle. LSTM is used to classify any event or behavior in crime or not. This live stream is also encrypted for more security. To make this system more secure with the use of a security algorithm. This system uses the Elliptic curve algorithm to protect data from any kind of manipulation. Through its ability to sense its surroundings, an autonomous vehicle is able to operate itself and execute critical activities without the need for human interaction. Video surveillance of computer vision technology can be used to take care of the security of the city by observing the crowd as well as individual activities. Much crowd-based crimes like mob lynching and individual crimes like murder, burglary, terrorism can be protected with advanced deep learning-based Anamoly detection techniques. Machine learning model use dataset for learning is taken from different sources specific for crime detection with the 90% accuracy.

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Violence Detection System using Convolution Neural Network

Cited by 4 publications

References 0 publications

Real time Violence and Weapon detection and Alert System

Real time Violence and Weapon detection and Alert System

A fully integrated violence detection system using CNN and LSTM

Autonomous Anomaly Detection System for Crime Monitoring and Alert Generation

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