Smart Surveillance System for Crisis Management

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025

p-ISSN: 2395-0072

www.irjet.net

Smart Surveillance System for Crisis Management Shubham Bangar1, Tushar Bansod2, Samarth Badadhe3, Nikhil Bhot4, Anant Bagade5 ¹Department of Information Technology, Pune Institute of Computer Technology, Pune, India ²Department of Information Technology, Pune Institute of Computer Technology, Pune, India ³Department of Information Technology, Pune Institute of Computer Technology, Pune, India ⁴Department of Information Technology, Pune Institute of Computer Technology, Pune, India ⁵Professor, Department of Information Technology, Pune Institute of Computer Technology, Pune, India ---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - With technology development at breakneck

trigger an automatic alarm to detect an emergency and mitigate the safety measures and time response. The project will focus on achieving an intelligent alerting surveillance system that would analyze both video and audio live data in real time by employing advanced AI models such as CNN, RNN, and SVM. This system would notify the relevant authorities during emergencies, including physical altercations, weapon usage, thefts, and distress signals, enabling prompt intervention.

speed, the need for advanced surveillance systems is become increasingly critical, especially when it comes to ensuring safety in high-risk environments. This work aims to develop Smart Surveillance and Crisis Detection tools for emergencies, so identifying weapon use, physical altercations, theft, and distress sounds, by evaluating real-time video and audio data using machine learning. It looks at the video and audio streams using artificial intelligence techniques using CNN, RNN, SVM, and YOLO for theft detection to help to identify significant events and notify relevant authorities.The project combines several detection models: object detection models forms the basis of theft detection, scream detection uses audio classification methods, and fight detection uses deep learning architectures. By means of training over several datasets, the system gains the capacity to consistently distinguish between regular tasks and emergencies. The main goals of the system are to increase public safety and response times in homes, stores, malls, and industrial sites. The proposed solution will create a reliable monitoring and emergency management segment and will do so with high efficiency and automation, thus enhancing the security level in various environments.

The core of this project is the fight detection module, which utilizes video inputs to identify violence between persons.

CNNs and RNNs among other deep learning approaches will be combined with traditional machine learning approaches in this work. While Recurrent Neural Networks (RNNs) handle sequential data, enabling the capture of temporal relationships in video sequences and the detection of the dynamics of physical altercations over time, Convolutional Neural Networks (CNNs) shine at extracting spatial features from video frames. Apart from video processing, the project also analyzes sound signals that may signify distress, such as human screams. For this purpose, the audio classification models classify the inputs after extracting features from the sound data based on the Mel-Frequency Cepstral Coefficients (MFCCs). These features are adept in capturing the spectral characteristics of sound and are conventionally used in speech and audio recognition applications. The classification process involves multiple machine learning algorithms such as SVM and Logistic Regression as well as several ensemble methods to classify the audio correctly as either a distress signal (for instance, a scream) or a non-distress sound.

Key Words: Smart Surveillance, Crisis Detection, Machine Learning, Video Analysis, Audio Analysis, Weapon Detection, Fight Detection, Scream Detection, Theft Detection, Deep Learning, CNN, RNN, SVM, YOLO, Public Safety, Emergency Response.

1.INTRODUCTION Safety and security have driven high-risk environments including malls, residential areas, businesses, and public spaces to expand quickly. This expanded market has been accommodating for intelligent surveillance systems. In contrast to being effective at monitoring, traditional surveillance systems usually lack the ability to detect and respond to a potential crisis in real-time. This realization has led to the intelligent development of surveillance systems powered by AI, which can not only surveil but also timely detect critical events such as weapon use, theft, physical altercation, fire, and even distress sounds.

Second, the weapon detection module is intended to detect the presence of weapons, which is a fundamental component in crisis management. The system utilizes the You Only Look Once (YOLO) object detection algorithm to scan and locate weapons in video frames, thus enhancing the ability of the system to detect potential threats in real-time. YOLO is particularly efficient and accurate in object detection operations, and thus it is an appropriate application in surveillance uses where speed of detection is of essence.

In addition, the theft detection module is another integral component of this system, with the responsibility of detecting suspicious behavior for theft, such as unauthorized entry or unauthorized removal of merchandise. The system makes

AI and ML have rapidly evolved to allow building higher autonomous systems for crisis detection. Such systems would

© 2025, IRJET

|

Impact Factor value: 8.315

|

ISO 9001:2008 Certified Journal

|

Page 677

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025

p-ISSN: 2395-0072

www.irjet.net

use of YOLO for real-time theft detection by means of its capacity to recognize particular objects or activities indicative of theft, such bags or removal without permission. In places like retail stores or high-risk areas where theft is common, this module is absolutely vital. YOLO is a very valuable tool for surveillance systems requiring quick and accurate identification since its effectiveness in spotting such circumstances is remarkable.

presented models maximizing accuracy and deployment on devices with limited resources, such as embedded systems.

Identifying Violence Notable contribution to the field of violence detection is the work of Moreira et al., which presented an end-to--end Bag-of- Visual-Words (BoVW) framework for detecting violence in surveillance footage. For classification they used Fisher Vector representation with Support Vector Machines (SVM), and for spatiotemporal interest point detection Temporal Robust Features (TRoF).

Prioritizing low memory consumption and real-time processing, especially for resource-limited hardware, this method shown better runtime and classification efficacy on the MediaEval Violent Scenes Detection (VSD) task dataset [1].

Trained and tested on a diverse dataset comprising events of actual violence, fight detection videos, scream audio files, weapon detection videos, and theft detection events, the system under discussion here will The dataset will expose the system to experience with regard for patterns for every distinct category of crisis scenario and enable effective generalization over many contexts and surroundings. By means of both video and audio processing of data, the system will offer a more complete method of crisis detection, so guaranteeing better accuracy and dependability in the identification of possible hazards.

By modifying Mobile Convolutional Neural Networks (CNNs) for embedded platforms such Raspberry Pi, Vieira et al. made significant progress in low-cost, real-time violence recognition. With an accuracy of 92.05% and a throughput of 4 frames per second, they made use of MobileNet, SqueezeNet, and NASNetMobile, so producing technologies fit for real-time use on resource-limited devices [2].

This project's primary objectives are 

to build an integrated surveillance system using video and audio inputs that can identify a broad spectrum of emergency events.



While using SVM, logistic regression, and more classifiers for audio-based crisis detection, analyze images and videos using deep learning architectures including CNNs, RNNs, and YOLO.



maximize the system for quick, low-latency processing to increase real-time detection efficacy.



Provide law enforcement departments, security guards, and other interested parties an easy interface so they may get real-time alarms and act accordingly.

To investigate the identification of actual fights in CCTV data, Perez et al. combined Temporal Robust Features with twostream CNNs and 3D CNNs. By means of temporal segmentation of vast surveillance data, their approach effectively identified violent episodes in real-world settings using datasets such as CCTV-Fights and Moments in Time [3].

Finding Armaments Using a customized weapons dataset, trained on the YOLOv3 object identification model, Narendrajo et al. have attracted attention for real-time weapon detection in surveillance systems. Their method combined socket programming with security infrastructure to enable real-time weapon detection using minimum computer resources [4].

This project's long-term objective is to create an intelligent, AI-based surveillance system so enhancing security and safety in sensitive surroundings. Real-time identification of possible crises made possible by this technology helps to possibly lower reaction times, increase public safety, and offer more consistent monitoring in cases when more traditional systems are insufficient. This project intends to contribute to the developing field of intelligent surveillance systems by integrating modern machine learning and deep learning techniques, so providing a real-world solution to one of society's main concerns: safety in real-time.

To extend the YOLO framework, Siri et al. similarly employed YOLOv4 in concert with machine learning and deep learning techniques. Their hybrid model showed great accuracy even with changing lighting by improving detection accuracy via Non- Maximum Suppression (NMS) [5]. Suganya et al. improved weapon detection using YOLOv7, so reducing false positives and increasing detection speed and accuracy, so proving its usefulness for application in several surveillance environments [6].

Identification of Theft Arora et al. built a motion-sensing system using machine learning classifiers to identify theft in surveillance footage. Their system delivered real-time alarms derived from camera inputs, so displaying its suitability for automated surveillance [7].

2. LITERATURE SURVEY Study of the Literature Recent advances in intelligent surveillance systems, using machine learning and deep learning approaches, have focused on real-time detection of theft, violence, and weapon presence. Many studies have

© 2025, IRJET

|

Impact Factor value: 8.315

To detect snatch theft in video feeds, Mandal and Choudhury compared deep learning models including AlexNet,

|

ISO 9001:2008 Certified Journal

|

Page 678

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025

p-ISSN: 2395-0072

www.irjet.net

GoogleNet, and ResNet variants. Their study clarified the choice of the most suitable models for precise theft detection from security cameras [8]. Using YOLO and CNN, Yeshwanth Reddy et al. created an artificial intelligence-driven framework for theft and robbery detection. Using pretrained models on customized monitoring datasets, the architecture focused on identifying unusual activity and generating alarms. Strong performance of their gadget under several environmental conditions made it suitable for useful purposes [9].

methods used in data augmentation on size and diversity to reduce overfitting risk during training..

3.2 Data Preprocessing Clean, normalize, and prepare the raw data for model input by means of data preprocessing; the following sub-steps comprise this phase:

Sound Detection Visual detection of audio-based techniques has been investigated in order to enhance surveillance systems. Arslan proposed Warped Linear Prediction (WLP) and Gaussian Mixture Models (GMM) based impulsive sound identification technique for real-time surveillance applications. His method improved detection accuracy [10] using the DCASE 2017 Task-2 data. With Mel Spectrograms and Short-Time Fourier Transform (STFT), Pratama et al. used CNNs to classify crimes and accidents from audio recordings with an accuracy of 93.337% [11].

Ojha and Venkateswar developed high accuracy for real-time scream detection by using Hidden Markov Models (HMM) in combination with Mel Frequency Cepstral Coefficients (MFCC) and Gaussian Mixture Models (GMM) in noisy environments [12]. Achieving over 90% accuracy in realtime, Dr. P.K. Venkateswar Lal et al. classified screams and distributed location-based alarms using Support Vector Machines (SVM) and Multilayer Perceptron (MLP [13]). Gao et al. improved scream recognition by using a hybrid CNNGRU model, so increasing detection accuracy and speed in noisy environments [14].

Mel-frequency cepstral coefficients (MFCCs) are extracted in order to concentrate on key sound patterns pertinent to distress signals and screams.



Frame Normalisation: Video frame pixel values are standardised to guarantee homogeneity across frames, so normalising the input.



Sound Enhancement: Audio signals are subjected to noise lowering methods to raise the quality and clarity of screams or distress sounds.

3.4 Model Architecture The system to be proposed uses a hybrid architectural approach that combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks to ensure that effective emergency detection is realized. CNN elements are used to extract spatial features of characteristics from a single frame of video, while LSTM elements are used to analyze the temporal aspect of the video, thus allowing detection of dynamic events such as altercations or weapon use. In weapon detection, as well as instances of theft, the model uses YOLO (You Only Look Once). YOLO is a state-ofthe-art object detection system that provides real-time detection ability, thus making it very effective in quickly detecting weapons and potential thefts in surveillance videos.

The system sends security agents immediate alarms when it detects weapons, knives, and other potentially dangerous obj ects.

3.1 Data Collection and Dataset Preparation The process of research begins with the collection of heterogeneous and large datasets, obtained from trusted repositories and surveillance networks. The datasets include a broad spectrum of emergency events, such as recordings of physical altercations, weapon usage, robbery cases, and distress calls. To impart heterogeneity and robustness to the dataset, the data is curate-d to gather samples from diverse settings, e.g., urban areas, shopping malls, residential areas, and industrial areas. Video frame interpolation, audio data conversion, and synthetic data generation are among the

Impact Factor value: 8.315



For video data, 2D Convolutional Neural Networks (CNN) are utilized to extract the spatial features for every frame in the video. Temporal features are also obtained using Recurrent Neural Networks (RNNs) so that the timeline can be interpreted. For audio data, feature extraction is carried out based on extracting changes in the frequency and amplitude of human cries and screams to make detection more accurate.

Short-time Fourier transform (STFT) and Mel-frequency cepstral coefficients (MFCC) are used.

The approach includes the design of an effective, stable, and scalable emergency detection system for intelligent surveillance. The system uses latest models like CNN, RNN, and YOLO for the detection of different emergencies like violence, use of weapons, theft, and distress calls to provide real-time detection with the least computation expense. The step-by-step procedure for developing and deploying the system is presented in this section.

|

Video Frame Extraction: Where unnecessary frames are eliminated, videos are broken up into frames to enable image-based processing.

3.3 Tokenization and Feature Extraction

3. METHODOLOGY

© 2025, IRJET



|

ISO 9001:2008 Certified Journal

|

Page 679

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025

p-ISSN: 2395-0072

www.irjet.net

Important characteristics of the model are :

3.6 Model Evaluation and Validation

CNN-LSTM Hybrid: Crucially for identifying events like physical altercations, CNN extracts features from individual frames while LSTM captures temporal dependencies across frames.

The resilience of the model over several data splits is validated using cross-valuation techniques including k-fold validation. Real-time testing is also conducted by implementing the system in a simulation environment where it receives live video and audio streams and processes them to measure performance in real-world conditions.

3.7 Real-Time Adaptability One of the most important aspects of the suggested system is real-time adaptability. The system is capable of working with live surveillance feed, processing the video and audio streams in real time. The model is retrained from time to time with new data to adapt to changing threats and changing patterns of emergencies so that the detection system stays in line with the changing times and effective in changing environments.

Fig. 1: CNN LSTM Architecture YOLO for Weapon and Theft Detection: YOLO models— more especially, YOLOv3 and YOLOv4—are tuned to identify weapons and possible theft situations. When weapons or suspicious objects (such stolen goods) are found, this model guarantees fast identification and alerting.

3.8 Handling Multilingual and Noisy Data Since the system is to be deployed in various environments, noisy and multilingual data processing must be done. Noise reduction algorithms are used on both audio and video data to eliminate redundant information and concentrate on the prominent features that signal emergencies. Multiple languages in audio and video data are also processed using multilingual models to enable the system to be deployable in vast numbers of locations.

3.9 Future Extensions and Real-Time Feedback Mechanisms The approach prepares one for improvements including:

Fig. 2: YOLO Architecture



Integration of External Data Sources: public safety reports—helps to improve detection accuracy and widen the range of emergency scenarios.



User Feedback for Continuous Improvement: Mechanisms built in allow users to report false alarms or missed detections, so enabling the system to grow by means of ongoing learning.



Multimodal Input Analysis: Extending the system to include more data types, such sensor data or metadata from surveillance cameras, will help to improve the general detection accuracy.



Scalability: Ensuring the system's capacity to scale for big-scale deployment over metropolitan areas by means of cloud-based infrastructures to effectively manage enormous data flows.

3.5 Classification and Decision Making After that, the CNN, LSTM, and YOLO model features are fed a decision-making layer that labels the event into predefined classes: fight, weapon detection, theft, or distress sound. The decision-making layer fuses the outputs from video and audio inputs using a multi-modal classification technique. A threshold-based decision plan is used to produce alarms based on the intensity of the event detected. 

Fight Detection: The CNN-LSTM model classifies videos into fight or non-fight categories based on visual and temporal cues.



Weapon and Theft Detection: YOLO sends alerts when knives, firearms, or suspicious behavior is found.



Scream Detection: Models trained on MFCC features classify audio data to detect whether distress sounds—such as screams—are present.

© 2025, IRJET

|

Impact Factor value: 8.315

Combining CNN, LSTM, YOLO for theft and weapon detection gives the suggested approach a complete, scalable, and effective solution for real-time emergency detection in smart surveillance systems.

|

ISO 9001:2008 Certified Journal

|

Page 680

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025

p-ISSN: 2395-0072

www.irjet.net

4. SYSTEM ARCHITECTURE

4.5 Feedback and Retraining Module

4.1 User Input and Submission

The retraining and feedback module is designed to enhance the system progressively using real-world feedback. Where there are misclassifications from security agents or users, the cases are added to the training dataset. The system utilizes the reported cases to retrain and update the models to enhance their accuracy. Through this, the system is able to learn new patterns, thus being effective in classifying new forms of crises, such as new threats or new ways of possessing weapons.

The technology starts with user real-time video and audio feeds.

This could live streaming, audio, or video feed from CCTV cameras. The input is flexible; video can be fed MP4 or MKV and audio in WAV or MP3. Streaming data can also be fed in for realtime crisis detection. There is a secure input channel that ensures data privacy and background validation checks are conducted to guarantee authenticity and timeliness of the input data for processing.

4.2 Preprocessing Stage The system starts with live audio and video inputs from the users. may be video input from CCTV cameras, audio, or live streaming It. The input is adaptable where video may be input in various formats such as MP4 or MKV and audio in WAV or MP3 formats. Streaming data may also be input for real-time crisis detection. There is an encrypted input channel to provide confidentiality of the data and background verification checks are conducted to authenticate and timely nature of the input data for processing.

4.3 Feature Extraction and Vectorization After preprocessing, the system extracts useful features to prepare the data for classification. For video data, features are extracted from individual frames in the spatial feature form through Convolutional Neural Networks (CNNs). The temporal relationship in the video sequence is then processed through Recurrent Neural Networks (RNNs) to enable the system to recognize events that occur over time, such as physical confrontations. For audio data, the MFCCs are processed to obtain the spectral features, which are vectorized to be used as an input to a machine learning model. The features are in numerical form to enable efficient processing.

Fig. 3: Flow Diagram 4.6 Output Stage After classification, the system sends real-time notifications to the responsible authorities. Classification outputs (such as "Fight Detected," "Weapon Detected," and "Scream Detected") and related data, such as location, occurrence time, and confidence levels, are included in the notifications. The system can provide a brief description for each classification, highlighting the characteristics or patterns in the video or audio that resulted in the classification output, in an effort to promote openness and user trust.

4.4 Classification with Deep Learning Models They are subsequently passed through specialized classifiers upon feature extraction. The fight detection module uses CNNs and RNNs to process the video data. The spatial feature extraction is done by CNN, while RNN processes sequential data so that the system may identify fight dynamics in real-time. The scream detection module utilizes audio classification models (SVM, Logistic Regression, etc.) to identify distress sounds such as screams. The weapon detection module utilizes machine learning-based YOLO (You Only Look Once) for object detection in real-time to identify weapons from video streams.

This feature allows security personnel to understand the cause behind the system's notification and make decisions on action priorities accordingly.

4.7 Performance Optimization To facilitate real-time performance, the system takes advantage of multiple optimization techniques. It applies GPU or TPUbased acceleration for deep models to parallel-compute video frames and audio features. Batch processing is utilized to process multiple streams or footage inputs, reducing latency and increasing throughput. The system leverages cloud-based infrastructure to offload workloads, affording scalability and fault tolerance, especially in high-traffic applications. Caching technologies cache preprocessed information, and models are

Both fight detection and weapon detection use a hybrid CNNRNN structure for video and a variety of machine learning models such as SVM, Random Forest, and XGBoost trained to identify screams or other distress signals properly in audio.

© 2025, IRJET

|

Impact Factor value: 8.315

|

ISO 9001:2008 Certified Journal

|

Page 681

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025

p-ISSN: 2395-0072

www.irjet.net

5. FUTURE VISION

optimized for low-latency processing, offering negligible delay in crisis detection.

5.1 Extension and Diversity of Datasets Expanding the datasets for model robustness is among the most crucial areas you still need work on in your system. including more varied surroundings, e.g., urban, rural, or even geographies with diverse cultural behaviors, will make the system better at real-world emergency condition detection in diverse environments. Including data from diverse camera angles, lighting, and resolutions will make the system more robust to real-world variations. Growing audio datasets to include more diverse sounds, e.g., various distress calls, alarms, or ambient sounds, will make realworld detection more accurate.

4.8 The Proposed System's Benefits The system provides a number of significant benefits: 

Real-time Detection: CNN, RNN, YOLO, and other classifiers are used to detect emergencies like fights, screams, and the use of weapons with high accuracy and speed.



Multi-modal Input: The system can offer a more thorough method of crisis detection by combining audio and video analysis.



Continuous Improvement: Based on actual usage, the feedback and retraining module allows the system to evolve and get better over time.



User Transparency: Giving security personnel confidence scores and alert explanations fosters trust and guarantees that they make well-informed decisions..



Scalability and Performance: The system's scalability and dependability in high-demand situations are guaranteed by the utilization of cloudbased infrastructure and performance optimization strategies.

5.2 Real-Time Crisis Detection and Response The system can be enhanced to incorporate real-time crisis detection capability, where video and audio inputs are processed in real time as they are being recorded. For example, if a weapon is being detected in a public place or a fight is being detected in real time, the system would be able to alert security guards and authorities in real time. With optimized quick-response streaming technology and machine learning algorithms, this feature would significantly reduce emergency response times, allowing authorities to act before the crisis spirals out of control. With the integration of the system into current public safety infrastructure (e.g., police, fire brigades, emergency medical services), communication would be seamless, and coordination during a crisis would be enhanced. Your system would then be a proactive, real-time crisis management system.

4.9 Difficulties and Remedial Action Though it has certain advantages, some difficulties could develop: 

Data Imbalance: Emergency and non-emergency events may not line up in the dataset. Artificial data generation, undersampling, or oversampling all help to solve this.



Model Generalization: The system has to be able to effectively traverse several crisis situations and surroundings. One can reduce this by using a varied training set including different situational data.



Computational Load: Real-time processing of video and audio could tax resources due their complexity.

These difficulties can be reduced by preprocessing edge devices and running intensive computations using cloud-based infrastructure.



5.3 Leveraging Ensemble Learning for Improved Accuracy To improve the accuracy of your detection models, ensemble learning techniques can be employed. By combining multiple models like CNN, RNN, and SVM, ensemble techniques can enhance detection by combining the strengths of each model.

Techniques like bagging, boosting, and stacking will make the system more robust against many forms of data inconsistencies and adversarial attacks. The use of ensemble techniques may also provide improved performance when faced with mixed levels of violence or distress signals, leading to a more robust system that can better discriminate between emergencies and non-emergencies.

5.4 Multi-modal Analysis and Cross-Validation The use of multi-modal analysis will also improve your system's performance. By combining audio and visual data, you can improve the accuracy of detecting some crisis situations, such as the identification of the sound of breaking glass in addition to the detection of movement or a weapon.

Also, cross-validation using data from multiple sources (e.g., CCTV cameras, drones, body-worn cameras) may provide a better interpretation of the context, making the system more able to discriminate between false alarms and real emergencies.

Environmental Variability: The system has to perform under different noise and lighting environments. Training on many data sources, dataset augmentation, and ongoing feedback help to reduce this problem.

© 2025, IRJET

|

Impact Factor value: 8.315

|

ISO 9001:2008 Certified Journal

|

Page 682

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025

p-ISSN: 2395-0072

www.irjet.net

5.5 Advanced Explainability and Transparency Features As your system makes high-stakes decisions, transparency and explainability are critical. Future versions of the system can incorporate visualization capabilities, showing the source of the reasoning behind the system's decision to trigger an alarm for a potential emergency. For instance, highlighting the movement pattern that resulted in the detection of a fight, or the exact audio frequency for a scream, would allow security personnel to better understand and respond to the alarms. Incorporating SHAP values or LIME (Local Interpretable Model-Agnostic Explanations) could also show more about how the model arrived at its decisions, enhancing transparency and trust in the system.

into full effect. Sentiment analysis from real-time audio and video streams can further make the detection of signs of distress or aggression possible, making detection of conflicts or threats at an early stage.

5.10 Ethical and Privacy Considerations Finally, because surveillance systems are an issue of concern for privacy, it must be ensured that the system is ethically sound and compliant with local privacy law. Future releases of your system must include privacy-preserving technologies, i.e., data anonymization or federated learning, to ensure individual rights are protected while, at the same time, you are still able to identify and react to crises. Having an ethical framework for data usage will make the system reliable and compliant with societal expectations.

5.6 Integration with Emergency Services and Social Media Your system can be integrated into emergency response systems for direct communication with law enforcement, medical services, or even public safety apps. Integration with social media platforms, like Twitter or Facebook, would allow the system to scan user-generated content and detect potential emergencies posted online. For instance, if a user posts a live video of a crisis, the system would detect distress signals and alert the concerned authorities in real-time. The integration would not only expand the scope of the system but also get a better view of the situation, including social media sentiment.

With the deployment of these innovations, your Smart Surveillance System and Crisis Detection project has the potential to become an end-to-end, scalable, and effective tool for security enhancement and crisis management globally.

These innovations will not only enhance the technical capabilities of the system but also provide a safer and more transparent environment, with the ability to revolutionize crisis detection and management in the digital age.

6. RESULT With the deployment of these innovations, your Smart Surveillance System and Crisis Detection project has the potential to become an end-to-end, scalable, and effective tool for security enhancement and crisis management globally. These innovations will not only enhance the technical capabilities of the system but also provide a safer and more transparent environment, with the ability to revolutionize crisis detection and management in the digital age.

5.7 Scalable and Lightweight Versions for Global Deployment To ensure global scalability, you can develop lightweight versions of the system that can run on lower-resource devices, such as smartphones or embedded systems. This would make the system accessible in regions with limited infrastructure, allowing it to be deployed in various parts of the world without the need for expensive hardware. A scalable system will be essential for widespread adoption in smart cities, industrial complexes, and even residential areas, where surveillance infrastructure may vary.

6.1 Scream Detection: Table 1: Scream Detection Results

5.8 AI-powered Behavioral and Sentiment Analysis To make the system scalable across the world, you can develop light-weight versions of the system that can be run on low-resource devices such as smartphones or embedded systems. This will make the system available in lowinfrastructure areas, and the system can be deployed in any corner of the world without the expense of high-end hardware. A scalable system will be essential to mass deployment in smart cities, industrial parks, and even residential complexes, where the surveillance infrastructure is heterogeneous.

5.9 Public Awareness and Training Modules The use of AI-based behavior and sentiment analysis can add more capabilities to the detection of emergency conditions.

Pattern analysis of user and crowd behavior (e.g., sudden movement, noise, sudden congregation) can make the system detect threats of impending crises, even before they come

© 2025, IRJET

|

Impact Factor value: 8.315

|

ISO 9001:2008 Certified Journal

|

Page 683

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025

p-ISSN: 2395-0072

www.irjet.net

The flexibility of the system offers it the potential to be utilized in a wide range of applications, from urban surveillance to industrial and residential security, where timely detection and intervention can reduce risks significantly. Moreover, the capability of the system to scale up for global deployment, including modules with different technological infrastructure, ensures its widespread use.

With the inclusion of real-time data streaming, ensemble learning techniques, and behavior analysis, the system has the potential to not only detect crises but predict and prevent them from escalating.

6.2 Fight Detection:

Interestingly, the project also addresses the global top challenges in privacy and data ethics, embracing privacypreserving approaches such as anonymization and compliance with local laws. The transparency and explainability of the system, including SHAP values for model decision-making, ensures additional user confidence and understanding, which is crucial for mass adoption in realworld applications.

Lastly, the project is a robust, scalable, and ethical solution to crisis identification in the physical and virtual worlds. It is a valuable tool for public safety enhancement through AI technology with continuing potential for enhancement through the integration of more sophisticated algorithms and more sources of data. As the system matures, its impact might be felt beyond emergency management to social welfare, urban planning, and public health, making societies safer and more informed worldwide.

CHART 1: Loss and Accuracy

7. COMPARISION TABLE Table -2: Comparison From Previous Work

REFERENCES

Improvements

Existing Work

Weapon detection done with YOLO with real time processing.

Weapon detection was mainly focused on R - CNN based

Combined audio video for decision-making with Integration.

Most previous works focused on either video or audio

Deployed the models real time.

Many prior studies research-based

[1]. D. Moreira, S. Avila, M. Perez, V. Testoni, E. Valle, S. Goldenstein, and A. Rocha, " Temporal Robust Features for Violence Detection" Proc. MediaEval Violent Scenes Detection (VSD) Task Dataset, 2019.

[2]. J. C. Vieira, A. Sartori, S. F. Stefenon, F. L. Perez, G. S. de Jesus, and V. R. Quietinho Leithardt, "Low-cost CNN for automatic violence recognition on embedded systems," Proc.

International Conference on Real-Time Embedded Systems, 2021.

were

8. CONCLUSIONS This project demonstrates the vast capability of machine learning algorithms, namely Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Support Vector Machine (SVM), in developing an end-to-end Smart Surveillance System and Crisis Detection tool. Using both video and audio inputs through advanced deep learning models, the system can detect crises such as fights, weapon usage, and distress calls efficiently, thus offering a proactive solution to improve safety in dangerous environments. The utilization of multiple models ensures high accuracy and immunity to different types of crisis situations, and real-time monitoring and alerting features offer an added layer of use for the system in responding to critical situations in realtime.

© 2025, IRJET

|

Impact Factor value: 8.315

[3]. M. Perez, A. C. Kot, and A. Rocha, "Detection of realworld fights in surveillance videos," Journal of Advanced Machine Learning, vol. 32, no. 4, pp. 123-134, 2020.

[4]. S. Narejo, B. Pandey, D. Esenarro, C. Rodriguez, and M. R. Anjum, "Weapon detection using YOLOv3 for smart surveillance systems," IEEE Transactions on Security Systems, vol. 12, no. 1, pp. 88-99, 2021.

[5]. D. Siri, P. B. Reddy, K. V. S. L. Harika, S. Ritwika, S. Sisodia, and K. Madhavi, "Automated weapon detection system in CCTV’s through image processing," IEEE Transactions on Image Processing, vol. 17, no. 3, pp. 240252, 2022.

|

ISO 9001:2008 Certified Journal

|

Page 684

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025

p-ISSN: 2395-0072

www.irjet.net

[6]. S. K. Suganya, P. A. Ranjani, S. A. Saktheswari, and R. Seethai, "Weapon detection using machine learning algorithms," IEEE Transactions on Surveillance Technologies, vol. 23, no. 6, pp. 98-110, 2022.

[7]. J. Arora, A. Bangroo, and S. Garg, "Theft detection and monitoring system using machine learning," International Journal of Video Surveillance Systems, vol. 18, no. 3, pp. 112124, 2021.

[8]. R. Mandal and N. Choudhury, "Snatch theft detection using deep learning models," Journal of Video Surveillance and Security, vol. 15, no. 4, pp. 134-145, 2021.

[9] Y. Reddy, S. Reddy, and S. R. Reddy, "AI-based automatic robbery/theft detection using smart surveillance," International Journal of AI in Security Systems, vol. 20, no. 5, pp. 45-59, 2022.

[10]. Y. Arslan, "A new approach to real-time impulsive sound detection for surveillance," Proc. DCASE 2017 Task-2, 2017.

[11]. A. A. Pratama, S. S. Sukaridhoto, et al., "Design of audiobased accident and crime detection," IEEE Transactions on Audio and Speech Processing, vol. 22, no. 6, pp. 210-223, 2021.

[12]. A. Ojha and P. K. Venkateswar, "Human scream detection for controlling crime rate using GMM and HMM," International Journal of Speech Processing, vol. 13, no. 4, pp.

56-70, 2021.

[13]. P. K. Venkateswar Lal, C. S. Sowmya, "Real-time human scream detection and analysis for controlling crime rate," Journal of Real-Time Signal Processing, vol. 19, no. 7, pp.

121-130, 2022.

[14]. J. Gao, Z. Liu, and S. Chen, "Automatic scream detection and classification using deep learning," IEEE Transactions on Audio and Speech Processing, vol. 27, no. 8, pp. 244-256, 2023.

© 2025, IRJET

|

Impact Factor value: 8.315

|

ISO 9001:2008 Certified Journal

|

Page 685