An Automated and Accurate Video Surveillance for Fast Violence

Detection Using Machine Learning

K. C. Rajavenkatesswaran, P. Abinaya, T. Kavinkumar, V. Libica and E. Nihethan

Department of Information Technology, Nandha College of Technology, Erode, Tamil Nadu, India

Keywords: Violence Detection, Real‑Time Monitoring, Public Safety, Deep Learning, Computer Vision, Surveillance

Systems.

Abstract: The main objective of this project is to create a real time fight detection system based on deep learning and

computer vision which can enrich the public safety in high-risk areas. But traditional CCTV surveillance has

human errors as well as inefficiencies, thus automated surveillance is necessary. It uses YOLO for fast and

precise object detection, live streams of video through a Flask backend system, extracts geolocation from live

video traffic, and immediately triggers alerts. The system provides real time surveillance and sends alert mail

to authorities with incident images at given timestamps and location. It seamlessly integrates into existing

security frameworks by reducing manual surveillance efforts. In addition, the system itself was designed to

be scalable for deployment in schools or transport hubs or other public places. Features that will be added in

future are multi camera integration, sound-based violence detection and predictive analytics working with AI

for proactive proactive crime prevention, a safer environment altogether. The public safety in high-risk area

is an increasingly important concern as traditional CCTV based surveillance suffers from prosities such as

being form humans such as being fatigue, delayed response, and misinterpretation of events. This project

presents a real time fight detection system based on deep learning and computer vision, which can help in

automation of violence detection and a faster and more easy response to security threats. Based on this, the

system is using the YOLO (You Only Look Once) algorithm for real time and highly accurate object detection,

which is able to analyze live video streams and identify the violent activities in real time.

1 INTRODUCTION

High risk areas like schools, transport hubs, public

spaces, with the potential for violence, and other

forms of crime, public safety is something people

really care about. Surveillance of these areas using the

current methods has been traditionally the use of

CCTV cameras, which are sometimes woefully

inadequate, recording things too late and oftentimes,

because of human error and inefficiencies. Security

dependency of the monitoring process creates high

risk on the fatigue of security personnel, that could

miss crucial incidents due to either distraction or

delayed response time. However, these limits indicate

a more efficient and automatic solution is required to

increase public safety. In this project, a real time fight

detection system is proposed that addresses these

challenges with help of deep learning and computer

vision techniques. In particular, the system utilizes

YOLO (You Only Look Once), a leading object

detection algorithm, that is fast and precise, to find

violent behavior in video streams from live video.

Bringing YOLO into play means incidents can be

identified immediately and a response does not have

to be initiated every time.

This combines a video stream with a Flask based

backend which processes video streams, extracts

geolocation, responds with instant alert in case of

fight. Once these alerts go out, the authorities get the

necessary information they need to react quickly;

these alerts include the images, time as well as the

location. This system automates detection and alert

process, reducing the manual work in monitoring and

improving the overall security operational efficiency.

The system has a scalable design that can be

deployed in different public and private settings such

as schools and large transportation centers. More

specifically, in the future, future analysis will be

conducted in deploying several camera feeds,

inclusion of sound-based detection for providing

more accurate identification of violent activities, and

the incorporation of predictive analytics accelerated

Rajavenkatesswaran, K. C., Abinaya, P., Kavinkumar, T., Libica, V. and Nihethan, E.

An Automated and Accurate Video Surveillance for Fast Violence Detection Using Machine Learning.

DOI: 10.5220/0013898900004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 3, pages

391-400

ISBN: 978-989-758-777-1

391

by artificial intelligence to forecast and ward off

cases. The solutions described in this project

constitute a major achievement in developing safer

environments via intelligent, automated surveillance.

The system is also intended for scalability in reducing

man power for the manual surveillance. In addition, it

can be easily deployed in public places, educational

institutions and huge transportation hubs.

Additionally, the system is open and expandable for

the addition of upcoming discoveries. Therefore, they

include the mixing of multiple camera feeds to benefit

from more coverage, the inclusion of sound-based

violence detection to increase accuracy, to the use of

AI driven predictive analytics, which utilize pattern

of behaviour detection to potentially thwart incidents

from happening before they occur. These will further

pave way for the system’s ability to adapt to dynamic

environments and offer complete public safety

solutions.

2 LITERATURE SURVEY

2.1 Video Vision Transformers (ViViT)

for Violence Detection

Video Vision Transformers (ViViT) emerged as a

possibility for video analysis and has opened a new

era for video analysis in the application of violence

detection. Serving as a variant of transformer-based

architectures, ViViT leverages transformer to process

video frames and capture temporal dependencies to

achieve great improvement in accuracy and

efficiency compared to traditional Convolutional

Neural Networks (CNNs). Because it’s difficult for

transformer models to transduce over long time spans

in videos, this is important for detecting incidents that

can spread for several frames, e.g., for violent

incidents (Redmon et al., 2016). In this paper, the

authors deal with this problem by data augmentation

strategy which artificially enhances the size of the

training data. The model’s performance is improved

in terms of ability to generalize to unseen data, and

especially on smaller datasets which are typically

used in real world applications.

By adopting this strategy, the model becomes

more stable and trusted as it is able to recognize all

kinds of violence in a context and in a noisy

environment. The results of this study show that

ViViT is viable for violence detection. Finally, the

authors demonstrate that their model overcomes old

object detection methods regarding speed and

precision, which makes it a viable option in the field

of real time monitoring in high-risk areas. This paper

demonstrates the feasibility of the use of deep

learning for understanding video through the

application of transformer-based models, while

setting the stage for future work in the application of

deep learning to public safety and surveillance

(Redmon et al., 2016).

2.2 YOLO Based Real-Time Fight

Detection Using Flask

In this paper, the author explores combining the

YOLO (You Only Look Once) object detection

algorithm with a Python based method, namely,

Flask, to produce a real time fight detection system.

Speed and accuracy in detecting objects in video

frames are what make YOLO popular as it does not

take much time to process video frames to produce

small boxes additional information about the object

that exists in the video frame. For instance, in this

study the videos in live feeds were used to detect

violent acts such as fights, instantly alerting security

person to any such scenes that happen (Zhao, W., &

Xu, Y. 2020). For video streams, we start using the

backend of the system, which is built in Python using

Flask, a lightweight web framework, to process the

video streams using key data: geolocation and

timestamps. As soon as an incident is detected, the

system triggers an alert that contains images and the

location of the incident and thus the authorities can

reach there instantly. The automation of this system

reduces the dependency on human surveillance and

its prevalent errors and delays that significantly

increase the efficiency of the whole survey. The

system is engineered to be very scalable to nearly any

public space, including schools, malls and

transportation hubs. The integration of this real time

detection system into current security systems can

greatly increase safety and security in the areas. It is

considered as a possible combination of advanced

object detection techniques with web-based solutions

to provide quick and timely alerts as well as improved

situational awareness for security personnel (Zhao,

W., & Xu, Y. 2020).

2.3 Convolutional Neural Networks

(CNNs) for Detecting Violent

Activity in Monitoring Videos

For detecting violent behaviors, video surveillance

has recently used deep learning techniques especially

Convolutional Neural Networks (CNNs). CNNs are

particularly suited for video frame pattern and feature

recognition to learn how to recognize, say, fight, other

types of violence. In this paper, the authors propose

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using CNNs for detecting violence in various real-life

situations (Li, X., Li, W., & Zhang, H. 2021). The

variability of the video data is one of the main

challenges for violence detection using deep learning.

The model signature of surveillance video can change

as a function of lighting, camera angle, resolution

etc., which may vary leading to a lesser accuracy. This

is addressed by the authors proposing means of

enhancing the robustness of CNN models, e.g. data

normalization and augmentation. The use of these

techniques will encourage the model to learn more

generalized features and more discriminate against

violence in different conditions. This paper shows

that CNNs can be highly effective in detecting

violence in surveillance videos, as stated by the

authors, and obtain high accuracy rates in a variety of

test cases. The potential applications of deep

learning-based violence detection systems on public

safety such as reducing the human security personnel

work load and reducing response times are also

discussed in the paper. The successful development of

this study emphasizes the role of the deep learning

and they are used to develop intelligent surveillance

systems (Li, X., Li, W., & Zhang, H. 2021).

2.4 AI Video Surveillance Systems for

Instantaneous Detection of Violence

Someone needed the help of AI powered systems to

detect violent incidents in real time as there has been

a need growing for public safety in crowded areas. By

way of example, this paper focuses on using artificial

intelligence (AI) to automatically analyze video feeds

from public spaces, including public parks, public

transport hubs, and shopping centers; and identify

violent behaviors. As part of this proposed AI system,

it processes live video streams via a combination of

the computer vision and machine learning algorithms,

which are sufficient in facilitating the ability of this

system to detect violent events, including a physical

fight or an assault, within seconds of their occurrence

(Wei et al., 2019). The key advantage of using AI for

violence detection is the ability to do so without

human intervention and thus reduce the oversight risk

and time delay response greatly. Using video feeds,

the system monitors the activities and alerts security

personnel immediately they detect something violent.

The authors point to the fact that AI based systems

can provide real time automated analysis in

conjunction with traditional surveillance

infrastructure while reducing the need for human

observation so as to catch the critical events. (Wei et

al., 2019)

2.5 Automated Fight Recognition

Using AI Enabled Surveillance

Systems

The study also points out the potential of integrating

the AI based violence detection systems into the

existing security frameworks and how this can help

during such emergencies. Doing this will greatly

improve public safety and reduce the security

personnel’s work load. According to the authors, this

technology could also be applied to different public

areas to improve the environment of the place in

which people are in high-risk areas. The algorithms

could be refined to handle more violent behaviors and

the system could be more easily adapted to other

environments (Wei et al., 2019). This system is

different from traditional video monitoring systems

that require humans to notice incidents, and detects

them automatically, which leads to faster response

times and decrease in chance for human error.

According to the authors, the system is trained on

huge datasets of surveillance footage in order to

detect the various types of violence and give exact

and effective detection (Gupta, S., & Koller, D.

2018).

2.6 CNN RNN Hybrid Model for

Violence Detection in CCTV

Footage

In this paper the authors demonstrate the AI based

fight detection systems which are promising as they

achieve high accuracy rates and low rates of false

positive. It results in realizing that such systems can

extend to improve public safety by notifying security

personnel of incidents in real time and thereby they

can respond quickly and effectively to incidents.

Finally, the paper concludes that AI driven fight

detection systems can be used as an addition to

traditional surveillance systems to modernize security

infrastructure (Gupta, S., & Koller, D. 2018).

Violence detection in a video surveillance, recently

has been applied the Convolutional Neural Networks

(CNNs) because they have been widely used for the

object recognition and the image classification tasks.

The aim of this paper is to explore the use of CNNs

to detect the violent actions in security cameras video

footage. Analyzing visual patterns in video frames,

the authors discuss that CNNs can be trained to

identify different kinds of violence: physical

altercations, assaults, and other kinds of aggressive

behaviour (Zhou et al., 2022).

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2.7 Real-Time Violence Recognition

Using Deep Learning Based Video

Stream Analysis

The diversity of the video data is one of the main

challenges to train CNNs for violence detection.

However, the videos can be captured from different

camera angles, under different lighting conditions and

at varied resolutions, and such variations could

impact the model’s ability of finding out the violence

which can be more difficult. To make up for this, we

suggest for using data augmentation, transferring

learning and multi scale feature extraction. These

approaches make it easier to learn robust features and

enhance the performance of the model in various

videos.

It is shown in the paper that CNNs can achieve

high accuracy in detecting violent action in

challenging surveillance environments. In addition,

the authors also discuss advantages of using deep

learning models for violence detection they are able

to operate automatically and continually without

surcharge on security personnel and increment

efficiency of surveillance systems. Finally, the study

highlights the possibility of using CNN based models

to provide aid in public safety and to integrate them

into existing security infrastructures to increase the

impact of crime prevention (Zhou et al., 2022). Based

on this, the authors in this study propose a deep

learning-based model for detecting violent incidents

in the real time video stream. The large dataset of

surveillance footage is trained on the model, which is

able to differentiate between what type of violence is

being perpetrated by identifying the physical fights,

assaults, and other forms of aggressive behaviour.

What makes this system unique is that it is able to

process video streams in real time and transmit to

security personnel and is intended to allow security

personnel to act quicker when violence is detected

(Chen, H., & Huang, X. 2020).

2.8 Violence Detection Using Deep

Learning Models: Comparative

Assessment

The authors explore the architecture of the deep

learning model, which incorporates a combination of

convolutional and recurrent layers to capture both

spatial and temporal features in the video data. By

combining these two types of layers, the model can

analyze the sequence of frames over time and identify

violent events that may evolve gradually. This

approach improves the model’s ability to detect

dynamic actions, such as fights that develop over

multiple frames, making it well-suited for real-time

monitoring in high-risk areas.

The results of the study show that the deep

learning model achieves high accuracy in detecting

violence, with a low rate of false positives. The

authors suggest that this technology could be

integrated into existing surveillance systems to

enhance public safety and provide real-time alerts to

security personnel. The paper concludes by

highlighting the potential of deep learning to

revolutionize video surveillance by enabling

automated, intelligent monitoring that can improve

the speed and effectiveness of response actions

(Chen, H., & Huang, X. 2020).

2.9 Violent Behavior Detection through

Machine Learning Based AI

Monitoring System

In the past, however, violence detection in

surveillance footage has gained significant interest

due to the growing interest in deep learning

approaches for video analysis. Convolutional Neural

Networks (CNNs), Recurrent Neural Networks

(RNNs) and hybrid models based on CNN and RNN

are some of the deep learning models used for the task

of detecting violent behaviors from videos which this

paper reviews. They compare the strengths and

weaknesses of each approach and then discuss how

they can work and are able to work in real world

violence detection scenarios (Sun et al., 2019).

CNNs are especially efficient at extracting spatial

features, deciding on visual patterns and objects

within the context of each individual frame.

Nevertheless, they are unable to grasp these temporal

dependencies amongst frames, which for JS action

detection is crucial, i.e. for instances that span over

time like fights. Thus, RNNs are combined with

CNNs to capture sequential data of video. The paper

argues that hybrid models that combine CNNs for

obtaining the feature and RNNs for sequence

modeling result in better performance for detecting

violence.

The review also outlines the issues that arise

during the training of deep learning models for

violence detection such as the requirement to have

large annotated datasets and the problem of detecting

subtle forms of violence. However, this problem is

underscored by the paper’s discussion on the promise

of deep learning models to lead the video surveillance

to revolution through more accurate and efficient

violent incident detection. According to the authors,

future research may be concerned with improving

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model generalization and finding new architectures

that are better at performing (Sun et al., 2019).

2.10 Machine Learning Based Hybrid

Model for Violence Detection in

Real Time Videos

In this paper, an AI powered surveillance system for

the real time prediction of the violent incidents is

presented based on machine learning algorithms. Live

video streams from security cameras can be analyzed

by the system and the system is capable of detecting

a variety of violent behaviors such as physical

altercation, assault and threat. The training is based

on large datasets of surveillance footage, so it is able

to detect wide range of violence and start sending

alarms in real time on seeing an incident happened

(Kumar, R., & Sharma, N. 2021).

The major advantage of this AI powered system is

its self-explanatory with less requirement to monitor

the surveillance tasks by the humans. The authors

discuss how such systems could be used as an

addition to traditional surveillance techniques since

they offer real time monitoring and alerts to security

personnel in a very short period of time. This helps in

faster action during an emergency and faster response

times thanks to this automated approach.

The paper discusses how the AI powered system

works to identify violence in different environments,

schools, malls and transport hubs among others.

Thus, the authors conclude that AI based surveillance

systems can improve on public safety by it provides a

more reliable, and efficient monitoring capability.

Also, they suggest that the system could be advanced

to have multiple cameras integrated as well as to

make predictive analytics to anticipate possible

events before they happen (Kumar, R., & Sharma, N.

2021). The authors of this study investigate how

machine learning can be used to perform real time

violence detection in video footage. A combination of

machine learning algorithms is used to analyze and

identify violent actions, like physical confrontations,

assaults, or threat in the proposed system. The system

allows videos to be processed in real time and receive

immediate feedback and triggers an alert in case of

violence.

The authors talk about the challenges of the

violence detection problem, for example large

annotated datasets and varying video quality. In order

to better capture the video data being used, multiple

algorithms in conjunction, using support vector

machines (SVMs) and decision trees, are suggested in

order to improve the detection accuracy. An attempt

to increase robustness and improve the model

performance on diverse environments (Zhang, Y., &

Tan, Y. 2021) is this hybrid approach.

Accuracy of the machine learning based system in

detecting violence is shown to be very high while the

false positive rate is minimal. The authors argue that

this methodology can be incorporated into current

surveillance systems for the benefit of public safety,

by alerting security personnel with real time

information of the incident. Finally, the paper

concludes with the remarks on the possibility of

machine learning to increase effectiveness of video

surveillance and reduce dependence on human

operators (Zhang, Y., & Tan, Y. 2021).

3 EXISTING SYSTEM

Violence detection systems rely on outdated

surveillance cameras that need a constant human

presence to manage them, which causes substantial

delays in actions taken, as well as human mistakes

that stem from exhaustion. Some violence detection

systems incorporate motion detection with rule-based

algorithms, but these systems frequently lead to false

alarms and are not robust in their real-world

applications. Most police departments usually look

through CCTV footage after an event has taken place,

which results in slower responses and minimization

of preventative measures. Existing systems highly

and in particular neglect cognitive analysis of actions

performed by the individual. This makes it very

difficult for such systems to differentiate between

violent and non-violent activities. Moreover, many

systems do not have an automatic alerting mechanism

that is directly linked to the emergency services

which, in turn, makes them less efficient in incident

prevention. Ethics and legalities surrounding

continuous surveillance restrict the adoption of these

systems, while privacy concerns and data security do

the opposite.

4 PROPOSED TECHNIQUES

In this paper, we propose the techniques and

methodology for the real time violence detection

system that would help to enhance public safety by

automatically identifying violent events occurring in

video streams. First the system integrates several

advanced technologies like deep learning models, real

time video processing, automates alerting

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mechanisms to form a robust solution for the

surveillance uses.

4.1 Deep Learning for Violence

Detection

Deep learning, particularly Convolutional neural

network (CNN) and Vision Transformer (ViT) are the

basis on our proposed system. These models are very

good at processing visual data and are capable of

automatically learning formidable features from

video frames. By using CNNs on individual frames

and ViTs on consecutive frames, connected spatio-

temporal information can be analyzed. By combining

the approach of both modulation and modulation and

gender analysis, one can identify violent behaviours

like physical fights, assaults and aggressive behaviour

with accuracy. A big data can be used to train these

models for different video instances of violence to

ensure it can generalize well to videos of violence that

it hasn't seen before.

Figure 1 shows the video streams of the violent

event identification. During the training phase, i.e., to

improve the model accuracy data augmentation

techniques like rotation, flipping and scaling are used

in training. By artificial means, these techniques

expand the spread of the training data to be more

robust for variations of video quality, camera angles,

and the environmental conditions. In addition,

transfer learning is applied to use pre-trained models

and fasten the training process with very little labeled

data while improving model performance.

Figure 1: Identifying Violent Events in Video Streams.

4.2 YOLO (You Only Look once) for

Real-Time Object Detection

In order to use for real time violence detection,

YOLO, an object detection algorithm considered

second to none for speed and accuracy, is integrated

into the system. YOLO provides object and action

detection ability in video frames at Realtime with

little latency. This enables fast response to

applications needing real time responses, such as

public spot security surveillance. In YOLO, the video

frame is broken up into a grid, potential objects in the

form of bounding boxes are grouped together in this

grid with the class prediction for each, with all this

done in a single forward pass through the model.

Because it is fast and accurate in detecting violent

activities it is very suitable.

For speed and precision, the system is using a

variant of either YOLOv4 or YOLOv5. The training

data consists of a large annotated dataset of the

images from various violent and non-violent

scenarios. With this, a custom trained YOLO model

is also incorporated to identify particular objects or

actions that are associated with violent behavior, like

physical altercations or aggressive movements. This

integration allows the system to react within seconds

when they happen to violent incidents and trigger

automated alerts and notifications.

4.3 Scalability and Integration with

Existing Infrastructure

Figure 2: Scalability and Integration.

One of the main features of the proposed system is

that it scales. It is to be deployed in a number of public

spaces including at schools, transport hubs, and in

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shopping malls. This makes it easily integrated with

existing CCTV infrastructure with an almost

seamless adoption without significant hardware

overhauls. The system can work with an an increasing

number of cameras and video streams due to the use

of cloud-based storage and adapting.

It is an excellent solution for the deployments of

such a large space that more than one camera would

be required to monitor each area separately. The

system can be expanded to have more cameras

attached and additional processing nodes to increase

scalability, so it continues to provide benefit as traffic

increases. In addition, it will be possible to add future

enhancements to the surveillance system, like the

sound-based violence detection, predictive analytics,

and multi camera coordination to further enhance the

effectiveness of the surveillance system. Scalability

and Integration plots are depicted in figure 2.

4.4 Accuracy of Detection

Accuracy of a violence detection system in

identifying violence is one of the most important

metrics for evaluating its effectiveness. Localizing

violence, often accompanied with shooting, has been

our motivation and we have proposed a combined

system based on a combination of real time object

detection (Video Using Optical Low Order) by

YOLO for real time detection and deep learning

models for violence recognition, which have

surpassed traditional rule base systems and older

machine learning models. Gathering information

through motion detection and simple threshold-based

algorithms is often insufficient as these traditional

methods have limited capability to analyze complex

scenes and especially miss subtle violent behaviors,

for instance one involving physical alteration or

aggressive gesture.

Figure 3: Accuracy of Detection.

On the other hand, deep learning-based models,

especially those trained with a large and diverse

dataset are better at recognising a variety of violent

act ranging from physical aggression to mild

aggression. In addition, YOLO's real time object

detection also minifies false positives in object or

action detection that does not qualify as violence. The

proposed system showed a detection accuracy of

about 95% by testing it rigorously on a great deal of

real-world streams which is well above the existing

systems that tend to sit in the 75 to 80 percent range

(figure 3).

4.5 Processing Speed and Latency

The real time performance is crucial for violence

detection systems, especially for high-risk area for

urgent response. The system proposed integrates

YOLO, which is very well known for its fast-

processing speeds and is able to analyze video frames

in real time. YOLO’s architecture has an architecture

that allows the system to process multiple frames per

second with remarkable little delay to triggering an

alert. The proposed system runs on video stream, on

average, at 30 frames per second (FPS) with latency

of less than 100 milliseconds per frame.

This pace is a lot quicker compared to older

detection procedures, which are a lot pricier with

regards to a computational burden since they involve

more costly calculations, such as the ones offered by

sliding window methods and traditionally employed

object tracking algorithms. While other deep learning

based advanced systems, subjected to other deep

learning models, such as Faster RCNN, could cater a

high accuracy, they lag behind when it comes to speed

for real time applications. That speed advantage of

YOLO ensures that our system will alert security

personnel quickly enough to respond to any incidents

in a timely manner.

Figure 4 gives the P_Curve,

R_Curve, F1_Curve, PR_Curve.

4.6 Detection Time and Response Time

In this paper, our proposed system denotes the

detection time as how soon the system can detect and

categorize violent events occurring in video in real

time. The system reduces detection time by using the

real time detection abilities of YOLO and deep

learning together. However, when a violent event

happens, the system can process and classify the

event within 1-2 sec before sending alerts (figure 5).

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Figure 4: P_Curve, R_Curve, F1_Curve, Pr_Curve.

As compared to traditional CCTV systems,

manual monitoring is commonly used, and this causes

a high delay between the incident and its detection.

They also tend to have slower response times due to

the fact that most automated systems without the use

of deep learning capabilities might need additional

time to process video frames and rely on simpler

algorithms that do not have the ability to efficiently

distinguish between normal and violent behavior. Our

system offers clear advantage by providing such near

instant detection and response as compared to these

traditional methods.

Figure 5: Labels.

5 RESULT ANALYSIS

The proposed framework outperforms the existing

system in terms of efficiency metrics with great

improvement. The addition of feature extraction

techniques such as Vision Transformer (ViT) and

RoBERTa for text processing has led to more

accuracy in the model. Such methods give the system

the ability to capture context at a deeper level which

helps in making accurate predictions.

There is significant improvement in recall,

precision, and F1-score. The existing system had

problems discriminating between some closely

coupled emotional states and hence organized them

incorrectly. The proposed approach incorporates a

joint based multi-modal fusion layer which processes

text, audio, video, and image data. This multi-modal

processing significantly decreases the false negative

and false positive rate, thus allowing the system to be

more confident in performing the tasks.

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6 CONCLUSIONS

The aim of this paper is to propose a real time

violence detection system which will improve public

safety on the high-risk areas by means of automated

surveillance. Combining the advanced deep learning

models, YOLO for real time object detection and

special neural networks for violence recognition, the

system accurately identifies and response to the

violence whether it occurs in a moving vehicle, a

fixed location, or in a remote area. The system

processes live video stream from surveillance

cameras to detect violent behavior happening live and

triggers immediate alert and thus minimizes the need

of involvement of human intervention in the manual

monitoring process.

The proposed system achieved high detection

accuracy, short processing time and short response

time compared with the traditional surveillance

systems, which require human oversight. The system

is able to identify a wide range of violent events such

as physical fights, assaults, violent behavior, using

fast, real time object detection with deep learning-

based action recognition on top of it. Additionally, the

system’s user interface accommodates an intuitive

interface for security personnel to easily securing and

response to incidents, and the system’s scalability can

be deployed at different public spaces like school,

transport hub or shopping mall.

Although the system provides considerable

advances in automated violence detection, there are

some avenues for further improvement and the work

is divided into potential future work. This includes

sound-based detection, predictive analytics to prevent

proactively predicated crime, multi camera

coordinated video to get better view of incidents, and

also strengthen the system’s ability to recognize

different types of complex actions. Moreover,

addressing privacy issues as well as ethical issues will

make sure the system is employed responsibly and in

conformance with legal regulation.

In general, the proposed real time violence

detection system is a powerful tool for enhancing the

public safety. This will automate the surveillance and

enable faster responses to violent incidents, thus

reducing the exposure risks to the public and make

public spaces safer for everyone.

7 FUTURE WORK

The integration of sound-based violence detection is

one of the promising areas of future development.

However, many of the current systems rely on the

visual data from the video streams and by adding the

corresponding audio signals, the detection

capabilities also improve. Additional indicators of

aggression involve sounds associated with violent

events such as loud shouting, physical impacts, or

breaking objects. The system by incorporating sound

analysis techniques like audio event detection and

speech recognition is capable of spotting violence

from obscured or unclear visual cues.

This enhancement would enable the system to

detect a violent event from wider a range of

situations, including cases where the camera angle or

conditions are not ideal. Sound based detection could

also be used in situations where a Visual analysis

alone may not provide enough context, e.g. for

domestic violence or noise disturbed environment.

Future work will be to gather and annotate large

sound-based violence dataset to train deep learning

models for this.

Future work in another area would be predictive

analytics of potential problematic violent incidents

that will occur beforehand. Using historical data such

as past violence events, behavioral patterns, and those

environmental factors the system could in theory

predict where and when violence is most likely to

occur. By leaving a journal of such incidents, security

personnel would be able to take preventive measures,

like intensifying patrols or contacting other

authorities, before the incident escalated.

To implement predictive analytics, machine

learning techniques like time series analysis,

clustering, anomaly detection are needed to be

integrated. If the system harnessed data from several

sources from earlier crime reports, environment (such

as crowded area in low lighting) and sensor data they

can generate insights for preemptive intervention.

This research will consist in developing algorithms

able to assign to a pattern of which violent behaviour,

and therefore give to the security teams the means to

act before violence happens.

Because the current system uses YOLO for real

time object detection it will be explored how within

the future, advanced techniques in object and action

recognition would aid to increase the system’s ability

to pinpoint signs of aggression or violence. Since

YOLO does an excellent job in object detection,

namely, people, vehicles, or weapons, it cannot detect

more intricate interactions like verbal confrontations

or physical fights at small scale.

The system will be able to recognize violent actions

such as pushing, hitting or aggressive gestures despite

ambiguity built from action recognition combined

with object detection. Based on these advanced

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techniques, the system will find it easier to identify

violence in more situations, ones that do not involve

violence that is obvious. This will further increase the

system's robustness for it to be able to handle many

more violent events in different environments by

integrating such models.

REFERENCES

Chen, H., & Huang, X. (2020). A Multi-View Video

Surveillance System for Action Recognition and

Violence Detection. Pattern Recognition Letters, 133,

174-181.

Gupta, S., & Koller, D. (2018). Detecting Violent Events in

Video Surveillance using Spatiotemporal

Convolutional Networks. IEEE Transactions on

Multimedia, 20(8), 2051-2064.

Kumar, R., & Sharma, N. (2021). AI-based Surveillance

Systems: Enhancements and Challenges in Detecting

Violent Events. International Journal of Artificial

Intelligence, 35(1), 98-114.

Li, X., Li, W., & Zhang, H. (2021). Real-time Violence

Detection using Deep Convolutional Neural Networks

and YOLO. Computers, Materials & Continua, 67(2),

1377-1390.

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016).

You Only Look Once: Unified, Real-Time Object

Detection. Proceedings of the IEEE Conference on

Computer Vision and Pattern Recognition (CVPR),

779-788.

Sun, Y., Wang, L., & Zhang, L. (2019). Audio-Visual

Fusion for Real-time Violence Detection in Video

Streams. IEEE Transactions on Signal Processing,

67(3), 578-589.

Wei, Z., Zhang, Y., & Wang, Q. (2019). Surveillance Video

Action Recognition with Deep Learning Models.

International Journal of Computer Vision, 127(6), 517-

530.

Zhang, Y., & Tan, Y. (2021). Privacy Preservation in

Surveillance Systems: Ethical Considerations for

Automated Violence Detection. Journal of Ethics in

Technology, 22(3), 215-227.

Zhao, W., & Xu, Y. (2020). A Survey of Violence

Detection in Video Surveillance. Journal of Visual

Communication and Image Representation, 73,

102886.

Zhou, Y., Wang, P., & Wang, Z. (2022). Predictive

Analytics for Public Safety: A Machine Learning

Approach to Violence Prevention. Journal of Artificial

Intelligence Research, 75, 345-368.

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