Fugitive Location Identification System Using Photo Matching Across Distributed Surveillance Networks

Abstract

Using image matching algorithms from distributed CCTV surveillance networks, this research suggests a criminal identification method. This method aims to increase the effectiveness and efficiency of identifying and apprehending criminals by using the capabilities of networked surveillance cameras. The distributed CCTV network and the matching algorithm make up the two primary parts of the suggested system. The vast array of security cameras that make up the distributed CCTV network are positioned in different areas, including streets, buildings, and public areas. These cameras provide data to a central computer by taking pictures of people in real time. The matching system analyzes the gathered pictures with a database of images of known offenders to identify potential matches. To ensure precision and efficiency, the system employs advanced techniques such as picture segmentation, feature extraction, and similarity evaluation. The proposed method addresses a number of concerns, including the sizeable surveillance network, the range of camera kinds and viewpoints, and privacy problems resulting from the storage and matching of individual photos. The suggested system shows encouraging outcomes in terms of criminal identification speed and accuracy through experimental assessments. The keywords for this paper are criminal identification, photo matching, distributed CCTV network, surveillance cameras, matching algorithm, feature extraction, image segmentation, similarity assessment, large-scale, diversity, privacy concerns, speed, accuracy.

Introduction

I. INTRODUCTION

The latest method for enhancing the accuracy and effectiveness of criminal identification in contemporary culture is the Criminal Identification System Using Photo Matching Across Distributed CCTV Surveillance Networks. Increased use of closed-circuit television (CCTV) networks by law enforcement agencies for public safety monitoring is a result of the networks' increasing installation in both public and private settings. It is quite difficult to identify and apprehend offenders, however, since security cameras capture a lot of film. The method uses picture matching technology to get around this problem by comparing the faces observed on CCTV video with a database of known offenders. The capacity of CCTV surveillance networks to function in a distributed fashion enhances the system's ability to detect criminals by granting access to several cameras located across different geographical areas. A strong database at the heart of the system houses an extensive collection of criminal records, complete with photos. Important face traits in these pictures allow for precise identification. In order to precisely compare and match the retrieved facial traits with the faces captured on the CCTV footage, sophisticated algorithms are utilized for analysis. By doing this, law enforcement officials may concentrate their efforts on verified matches and reduce the number of false positives, which will save a substantial amount of time and money. Because of the distributed architecture of the system, law enforcement authorities can get a complete picture of criminal activity in real- time by accessing CCTV footage from multiple places. The authorities are notified immediately upon the discovery of a possible match, facilitating prompt and efficient action. The efficiency of the system can also be increased by processing multiple matches at once. Furthermore, the system is built with data security and privacy in mind. The database is only accessible by authorized law enforcement officials, guaranteeing the security of critical data. Strict procedures are also in place to control data deletion and retention, guaranteeing adherence to privacy laws.

A notable technological development in the fields of criminal identification and CCTV surveillance is the Criminal Identification System Using Photo Matching Across Distributed CCTV Surveillance Networks. Law enforcement organizations may effectively identify and apprehend offenders by utilizing the vast network of CCTV cameras and photo matching technologies, thereby augmenting the general safety and security of society. This technology, which offers improved accuracy, efficiency, and efficacy in criminal identification, is a useful weapon in the battle against crime thanks to its clever algorithms and real-time capabilities.

II. RELATED WORKS

1. In a cloud-IoT-based computing environment, video clips and UAV photos are used in a hybrid strategy for suspicious object surveillance, as explained in the first article. The paper suggests a solution for real- time object detection and monitoring that blends video clips with UAV photos. The writers stress how improving surveillance systems can benefit greatly from cloud computing and Internet of Things technology.
2. An overview of video surveillance systems in smart cities is given in the second article. An overview of several video surveillance systems and how they are used in smart city settings is given in this paper. The writers talk about the difficulties and potential applications of video surveillance systems in smart cities.
3. In order to detect weapons in video surveillance systems, the third piece focuses on combining software-defined networking with IoT and fog computing based on deep learning. The study suggests a framework to increase the precision and effectiveness of weapon identification in surveillance footage by combining various technologies.
4. The automated criminal identification system utilizing facial recognition and detection is presented in the fourth article. The goal of the project is to create a sophisticated system that can recognize offenders on sight by examining their facial traits. The system's possible uses in law enforcement are emphasized by the authors.
5. The fifth article provides a thorough analysis of human re-identification strategies, addressing several facets of this technology. The study examines the effectiveness and drawbacks of several human re-identification algorithms and techniques.
6. The sixth essay focuses on criminal suspect identification and facial detection using deep learning. The paper suggests a mechanism for identifying and detecting criminal suspects from surveillance footage by using deep learning algorithms. The writers emphasize how precise and effective their method is.
7. The seventh article investigates how blockchain technology can be integrated with Internet of Things (IoT) to provide a safe chain-of-custody procedure for digital forensics. The paper suggests a method for guaranteeing the security and integrity of digital forensic data that uses blockchain hyperledger Sawtooth.
8. In the eighth article, multinet architecture- based intelligent video surveillance systems for vehicle identification are covered. The paper suggests a solution for precise and effective vehicle recognition in surveillance footage by utilizing deep learning algorithms and multinet architecture.
9. The ninth article offers a thorough analysis of current developments in rail network surveillance video analytics, with an emphasis on security, trespassing, and suicide prevention. The writers go over several methods and algorithms for video analytics in rail network monitoring and emphasize how they enhance security and safety.
10. A method for detecting suspicious activity using surveillance footage and an improved CNN is presented in the tenth article. The paper suggests a method for precisely identifying suspicious behavior by using an improved Convolutional Neural Network (CNN) model. The system's efficacy and dependability are highlighted by the writers.

Please be aware that the summaries given above are only cursory and do not directly credit the authors.

III. EXISTING SYSTEM

There are a number of serious drawbacks to the current criminal identification approach that uses photo matching over dispersed CCTV surveillance networks. First off, a major component of the system's dependability is the CCTV cameras' precision. There are several CCTV cameras that are frequently utilized in surveillance networks, however their image quality and resolution are limited. This may result in limited visibility and pixelation in the photos that are taken, which makes it challenging to get clear and useful photos for identification.

Additionally, the method relies on having a complete and current database of criminal photos that may be compared. But keeping up with such a database is no easy feat. Regular updates to the database are required for new images and information on wanted offenders. Furthermore, cross-referencing the database with the CCTV camera photographs may be costly and time-consuming due to the high processing power and resource requirements.

The existing technique's high likelihood of producing false positives and false negatives is another disadvantage.

A number of things may cause incorrect matching and identification, including low- quality images, uneven lighting, disguises, and changes in physical appearance. It might be detrimental to the public's confidence in the system as well as the people involved if false positives result in law-abiding folks being mistakenly classified as criminals. On the other hand, since they let criminals evade detection, false negatives endanger public safety.

The existing approach also raises concerns about privacy and civil rights. People's rights may be violated and unwarranted spying may occur if they are constantly being identified and watched over by CCTV surveillance networks that are spread out.

These worries may negatively affect how the public views and accepts the system, which could eventually affect how effective it is.

In addition, the current system's operation necessitates a large infrastructure and communication across several surveillance networks. Technical and logistical difficulties may arise from this, especially in places with little infrastructure or resources.

To sum up, there are a number of issues with the current Criminal Identification System that affect its ability to maintain and accurately match photos across dispersed CCTV surveillance networks, including image quality limitations, the possibility of false matches, privacy concerns, and infrastructure requirements. Resolving these issues is essential to creating a criminal identification system that is more dependable and effective.

IV. PROPOSED SYSTEM

Through the use of photo matching techniques across dispersed closed-circuit television (CCTV) surveillance networks, the proposed work seeks to establish a Criminal Identification System. Using the vast volumes of CCTV video that are collected by various security cameras, the goal is to increase the effectiveness and efficiency of criminal identification. The technology will use advanced image processing and computer vision algorithms to quickly and accurately identify potential offenders by automatically matching suspect photos with real-time video feeds from scattered security cameras. The scattered functioning of the proposed system would provide smooth integration and cooperation across different surveillance networks, such as government-owned, commercial, and public CCTV systems.

This integration will preserve privacy and security concerns while guaranteeing a larger coverage area and improved possibilities of identifying perpetrators. Face detection and recognition algorithms, picture database administration, distributed data processing, and real-time video analysis are some of the system's essential parts. Algorithms for face detection and recognition will automatically identify faces of people and extract facial attributes to compare to the criminal photo database that is currently in place. Large amounts of surveillance footage will be stored and indexed by the picture database management, making it possible to compare and retrieve suspicious photos quickly. The system's scalability and performance will be enhanced by the distributed data processing module's capacity to carry out complex calculations and analysis across several surveillance networks. In order to promptly notify law enforcement agencies of any suspicious activity, the real-time video analysis component will continuously watch the CCTV feeds. Law enforcement officers will also be able to search and explore the criminal photo database using an intuitive interface, and generate reports and analytics for their investigative needs. In order to improve public safety and security, this proposed study will help build a strong and effective Criminal Identification System that makes use of distributed CCTV surveillance networks and sophisticated photo matching algorithms.

V. SYSTEM ARCHITECTURE

VI. METHODOLOGY

A. Module for Pre-processing and Image Acquisition

First is the Image Acquisition and Pre- processing module of the Criminal Identification System. The distributed closed-circuit television (CCTV) surveillance networks' picture collection is handled by this module. In order to build a comprehensive database for criminal identification purposes, the system attempts to gather pictures from several CCTV cameras in different places. In order to guarantee crisp and consistent pictures for further processing, this module comprises methods like video frame extraction, noise reduction, and image enhancement.

The module can also manage various picture formats and resolutions, which is useful for networks with CCTV surveillance that are heterogeneous.

VFE(V) = {F1, F2, ..., FN} for video V with N frames, extracting each frame Fi.

NR(F) = F' applies noise reduction to frame F, producing F'.

IE(F') = F'' enhances the noise-reduced frame F', resulting in F''.

IRFS(F'', r, f) = F''' standardizes image F'' to

resolution r and format f, resulting in F'''.

B. Face Recognition and Matching Module

This module is the second in the proposed Criminal Identification System. After the photographs have been pre-processed, this module uses sophisticated face recognition algorithms to locate and extract the images' facial characteristics. Next, a database of known offenders and suspects is compared with these face attributes. Assessing the similarity between the retrieved face characteristics and the features kept in the database is a step in the matching process. The module achieves precise and dependable face matching by using methods like eigenface, principal component analysis (PCA), and deep learning-based approaches. An alert is sent to the appropriate authorities informing them of the possible presence of a criminal in the monitored region if a match is discovered.

FFE(I) = Fi, where Fi are facial features from image I.

FC(Fi, Fd) = S, similarity score between extracted features Fi and database features Fd.

PCA(F) = F_PCA, principal components of facial features F.

EF(F) = F_EF, eigenfaces from facial features F.

DLA(F) = F_DL, deep learning model's extracted features from F.

AT: If S >= threshold, then alert is triggered.

C. Database Management

The module responsible for database management and system integration is the third and last one in the Criminal Identification System. The enormous volume of data gathered from the CCTV surveillance networks must be stored and managed by this module. It makes sure that data is efficiently indexed and retrieved so that perpetrators may be quickly identified. In accordance with ethical and regulatory standards, the module also has procedures for data security, privacy protection, and backup. In order to improve cooperation between various agencies, the module also makes interface with current criminal databases or other law enforcement systems easier. Faster reaction times and more efficient crime prevention and control are made possible by this connection, which facilitates seamless information exchange and real-time updates.Law enforcement agencies can better prevent crime, enhance public safety, and improve criminal identification capabilities by utilizing advanced image processing and face recognition technologies through the integration of these three modules into the Criminal Identification System Using Photo Matching Across Distributed CCTV Surveillance Networks.

DS(D) = {I1, I2, ..., IN} for dataset D with N images, storing each image Ii with an index.

EI(Q) = {I | I matches Q} for a query Q, optimizing search within the database.

DSP(D) = D' encrypts or anonymizes data D to protect privacy.

BR(D) = D_backup ensures data recovery in case of loss.

SI(S1, S2, ..., Sn) = S_integrated integrates systems for seamless data exchange.

RTU(E) = Update in S_integrated, for event E, ensuring immediate information sharing.

D. Deep CNN

A Deep Convolutional Neural  Network (CNN) plays a pivotal role in the Criminal Identification System using Photo Matching across Distributed CCTV Surveillance Networks. First and foremost, these networks excel at extracting intricate features from images, particularly relevant in facial recognition tasks. By analyzing the arrangement of facial components such as eyes, nose, and mouth, as well as other distinguishing features, CNNs facilitate accurate identification of individuals across various surveillance cameras. Furthermore, their adeptness at pattern recognition is indispensable for matching faces captured by CCTV cameras with those stored in the system's database. Through extensive training on large datasets, CNNs become proficient at distinguishing between faces of interest, such as known criminals, and non- relevant faces. Moreover, the robustness of CNNs to variability in lighting conditions, facial expressions, poses, and occlusions is paramount in surveillance environments, ensuring reliable performance despite challenging conditions. Additionally, CNN architectures are highly scalable, capable of accommodating large-scale surveillance systems with distributed CCTV networks efficiently. With advancements in hardware acceleration, these networks can be deployed on edge devices or in distributed computing environments, enabling real-time processing of video streams from multiple cameras without significant latency. Furthermore, CNNs are adaptable, capable of being fine- tuned or retrained with additional data to enhance performance over time, ensuring continued efficacy in identifying individuals of interest and maintaining public safety and security.

D. Limitations and Future Directions

Finally, the output section acknowledges any limitations or challenges encountered during the implementation of the system and proposes avenues for future research and improvement. This may include addressing issues such as scalability, computational efficiency, and privacy concerns associated with CCTV surveillance. Additionally, suggestions for enhancing the system's capabilities through the integration of emerging technologies such as artificial intelligence, deep learning, or sensor fusion techniques may be discussed, paving the way for more robust and versatile fugitive location identification systems in the future.

IX. FUTURE WORK

The Criminal Identification System Using Photo Matching Across Distributed CCTV Surveillance Networks is an innovative and advanced system that aims to enhance law enforcement agencies' ability to identify criminals in an efficient and cost-effective manner. This system utilizes cutting-edge facial recognition technology and a distributed network of surveillance cameras to  collect  real-time  video  footage  and compare it with a centralized database of criminal photos. By analyzing facial features and matching them with known criminals, this system enables authorities to quickly identify and apprehend suspects. The distributed nature of the CCTV surveillance networks ensures comprehensive coverage and enables rapid data sharing across multiple locations. This system offers numerous benefits, including improved public safety, reduced crime rates, and enhanced investigation capabilities. Moreover, it minimizes the need for extensive manual efforts by automating the process of criminal identification. The future work pertaining to this system includes further research and development to enhance the accuracy and reliability of facial recognition algorithms. Additionally, the system's scalability and interoperability can be improved, allowing for seamless integration with existing surveillance infrastructure. Furthermore, ethical and privacy concerns related to the use of facial recognition technology need to be addressed through policy development and adherence to legal frameworks.

Conclusion

Finally, the Criminal Identification System that matches photos across dispersed CCTV monitoring networks provides law enforcement organizations with a great deal of improvement. Through the use of sophisticated picture recognition algorithms, the system is able to match and identify suspects in a variety of places, resulting in faster reaction times and more efficacy in the tracking and capture of offenders. Effective data exchange and cooperation between various surveillance systems are ensured by the usage of dispersed networks, which eventually improves crime prevention and detection. Law enforcement can react swiftly to new threats since the technology permits real-time identification and lessens the need on laborious human identification techniques. All things considered, this technology has enormous promise for raising public safety and improving the general effectiveness of criminal identification procedures.

References

[1] Ahamad, R., & Mishra, K. N. (2023). Hybrid approach for suspicious object surveillance using video clips and UAV images in cloud-IoT-based computing environment. Cluster Computing, 1-25. [2] Myagmar-Ochir, Y., & Kim, W. (2023). A survey of video surveillance systems in smart city. Electronics, 12(17), 3567. [3] Fathy, C., & Saleh, S. N. (2022). Integrating deep learning-based iot and fog computing with software-defined networking for detecting weapons in video surveillance systems. Sensors, 22(14), 5075. [4] Randhir, K. P., Ramesh, O. S., Sanjay, L. S., Gangadhar, K. G., & AP, S. AUTOMATED CRIMINAL IDENTIFICATION SYSTEM USING FACE DETECTION AND RECOGNITION. [5] Singh, N. K., Khare, M., & Jethva, H. B. (2022). A comprehensive survey on person re-identification approaches: various aspects. Multimedia Tools and Applications, 81(11), 15747-15791. [6] Sandhya, S., Balasundaram, A., & Shaik, A. (2023). Deep Learning Based Face Detection and Identification of Criminal Suspects. Computers, Materials & Continua, 74(2). [7] Khan, A. A., Shaikh, A. A., & Laghari, A. A. (2023). IoT with multimedia investigation: A secure process of digital forensics chain-of-custody using blockchain hyperledger sawtooth. Arabian Journal for Science and Engineering, 48(8), 10173-10188. [8] González-Cepeda, J., Ramajo, Á., & Armingol, J. M. (2022). Intelligent video surveillance systems for vehicle identification based on multinet architecture. Information, 13(7), 325. [9] Zhang, T., Aftab, W., Mihaylova, L., Langran-Wheeler, C., Rigby, S., Fletcher, D.,... & Bosworth, G. (2022). Recent advances in video analytics for rail network surveillance for security, trespass and suicide prevention—A survey. Sensors, 22(12), 4324. [10] Selvi, E., Adimoolam, M., Karthi, G., Thinakaran, K., Balamurugan, N. M., Kannadasan, R., ... & Khan, A. A. (2022). Suspicious actions detection system using enhanced CNN and surveillance video. Electronics, 11(24), 4210. [11] S. T. Ratnaparkhi, P. Singh, A. Tandasi and N. Sindhwani, \"Comparative Analysis of Classifiers for Criminal Identification System Using Face Recognition,\" 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 2021, pp. 1-6, doi: 10.1109/ICRITO51393.2021.9596066. [12] S. T. Ratnaparkhi, A. Tandasi and S. Saraswat, \"Face Detection and Recognition for Criminal Identification System,\" 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2021, pp. 773-777, doi: 10.1109/Confluence51648.2021.9377205. [13] S. Shilaskar, V. Shelke, S. Bhatlawande, P. Shinde and K. Shintre, \"Robust Criminal Identification System for Recognition of Obscure and Hidden Faces,\" 2023 2nd International Conference on Futuristic Technologies (INCOFT), Belagavi, Karnataka, India, 2023, pp. 1-6, doi: 10.1109/INCOFT60753.2023.10425451. [14] S. Jagtap, N. B. Chopade and S. Tungar, \"An Investigation of Face Recognition System for Criminal Identification in Surveillance Video,\" 2022 6th International Conference On Computing, Communication, Control And Automation (ICCUBEA, Pune, India, 2022, pp. 1-5, doi: 10.1109/ICCUBEA54992.2022.10010987. [15] M. F. Abdullah Al Sohan, N. J. Anannya, A. Nahar and K. A. Kalpoma, \"Preliminary Findings: Use of CNN Powered Criminal Identification System,\" 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Maldives, Maldives, 2022, pp. 1-6, doi: 10.1109/ICECCME55909.2022.9988423. [16] L. Dang, M. Yang, B. Si and K. Yi, \"Blurred Portrait Processing Technology for Public Security Criminal Based on MATLAB,\" 2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi\'an, China, 2023, pp. 225-228, doi: 10.1109/ICSP58490.2023.10248870. [17] A. F. Nyoka et al., \"Reliable Face Identification System for Criminal Investigation,\" 2023 11th International Symposium on Digital Forensics and Security (ISDFS), Chattanooga, TN, USA, 2023, pp. 1-5, doi: 10.1109/ISDFS58141.2023.10131738. [18] E. Sacchetto, \"Doctoral Consortium Proposal: : Biometrics as forensic evidence: some reflections from the Italian Criminal proceeding’s point of view,\" 2020 8th International Workshop on Biometrics and Forensics (IWBF), Porto, Portugal, 2020, pp. 1-4, doi: 10.1109/IWBF49977.2020.9107948. [19] Shafia and M. A. Chachoo, \"Social Network Analysis Based Criminal Community Identification Model with Community Structures and Node Attributes,\" 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2022, pp. 334-339, doi: 10.1109/ICSSIT53264.2022.9716286. [20] S. M. Rajesh, I. Chiranmai and N. Jayapandian, \"Machine Learning Based Crime Identification System using Data Analytics,\" 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA), Theni, India, 2023, pp. 951-956, doi: 10.1109/ICSCNA58489.2023.10370717.

Copyright

Copyright © 2024 Ajay Krishna K A, Purushothaman D , Prranith Saidapeta . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.