Authors :
Neha Khatri; Bhanupriya Thakur; Sagar Sharma; Bhaskar Jha
Volume/Issue :
Volume 10 - 2025, Issue 9 - September
Google Scholar :
https://tinyurl.com/mrs84n92
Scribd :
https://tinyurl.com/4rr3zt65
DOI :
https://doi.org/10.38124/ijisrt/25sep095
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Note : Google Scholar may take 30 to 40 days to display the article.
Abstract :
Vehicle theft remains a significant global issue, where current solutions often fall short due to their overreliance on
GPS trackers, manual monitoring, and disconnected law enforcement systems. This paper presents a comprehensive AI-
controlled monitoring structure that revolutionizes detection and tracking of vehicles. The proposed system integrates
computer vision in real time using CCTV and drone recording, future indicative analysis for route assessment and blockchain-
based verification of vehicles. Unlike traditional methods, our solution identifies stolen vehicles, even with converted license
plates or missing GPS devices, leverages visual features such as models, color and damage patterns. In addition, a law
enforcement dashboard ensures immediate notice and spontaneous coordination. Experimental assessment shows high
identification accuracy, reduces false positivity and increases the reaction rate, making it a viable candidate for smart city
infrastructure. This proposed surveillance system has significant potential to prevent crime, urban traffic management and
insurance confirmation, for safe and more responsible urban environment.
References :
- License Plate Recognition (LPR)
- Zherzdev, S., & Gruzdev, A. (2018). LPRNet: License Plate Recognition via Deep Neural Networks. arXiv. https://arxiv.org/abs/1806.10447arXiv
- Zhang, L., Wang, P., Li, H., Li, Z., Shen, C., & Zhang, Y. (2020). A Robust Attentional Framework for License Plate Recognition in the Wild. arXiv. https://arxiv.org/abs/2006.03919arXiv
- Laroca, R., Zanlorensi, L. A., Gonçalves, G. R., Todt, E., Schwartz, W. R., & Menotti, D. (2019). An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO Detector. arXiv. https://arxiv.org/abs/1909.01754arXiv
- Wang, Y., Bian, Z.-P., Zhou, Y., & Chau, L.-P. (2020). Rethinking and Designing a High-performing Automatic License Plate Recognition Approach. arXiv. https://arxiv.org/abs/2011.14936arXiv
- Vehicle Re-Identification and Object Tracking
- Chen, Y., et al. (2021). A Deep Learning Model of Dual‐Stage License Plate Recognition. Wiley. https://onlinelibrary.wiley.com/doi/10.1155/2021/3723715Wiley Online Library
- Zhao, H., et al. (2020). License Plate Recognition System Based on Improved YOLOv5 and GRU. ResearchGate. https://www.researchgate.net/publication/367606366_License_Plate_Recognition_System_Based_on_Improved_YOLOv5_and_GRUResearchGate
- Drone Surveillance and Predictive Analytics
- Lim, J. (2022). Latency-Aware Task Scheduling for IoT Applications Based on Artificial Intelligence with Partitioning in Small-Scale Fog Computing Environments. Sensors. https://www.researchgate.net/publication/363945254_Latency-Aware_Task_Scheduling_for_IoT_Applications_Based_on_Artificial_Intelligence_with_Partitioning_in_Small-Scale_Fog_Computing_EnvironmentsResearchGate
- Blockchain Integration in Vehicle Verification
- Yiran Chen. (n.d.). Duke Electrical & Computer Engineering. https://ece.duke.edu/people/yiran-chen/Duke Electrical & Computer Engineering
- General AI and Cybersecurity Applications
- Frontiers in Computer Science. (2022). Toward Immersive Communications in 6G. https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2022.1068478/fullFrontiers
Vehicle theft remains a significant global issue, where current solutions often fall short due to their overreliance on
GPS trackers, manual monitoring, and disconnected law enforcement systems. This paper presents a comprehensive AI-
controlled monitoring structure that revolutionizes detection and tracking of vehicles. The proposed system integrates
computer vision in real time using CCTV and drone recording, future indicative analysis for route assessment and blockchain-
based verification of vehicles. Unlike traditional methods, our solution identifies stolen vehicles, even with converted license
plates or missing GPS devices, leverages visual features such as models, color and damage patterns. In addition, a law
enforcement dashboard ensures immediate notice and spontaneous coordination. Experimental assessment shows high
identification accuracy, reduces false positivity and increases the reaction rate, making it a viable candidate for smart city
infrastructure. This proposed surveillance system has significant potential to prevent crime, urban traffic management and
insurance confirmation, for safe and more responsible urban environment.