Multi-Modal AI Architecture for Real-Time Detection and Tracking of Stolen Vehicles


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

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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)
  1. Zherzdev, S., & Gruzdev, A. (2018). LPRNet: License Plate Recognition via Deep Neural Networks. arXiv. https://arxiv.org/abs/1806.10447arXiv
  2. 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
  3. 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
  4. 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
  1. 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
  2. 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
  1. 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
  1. Yiran Chen. (n.d.). Duke Electrical & Computer Engineering. https://ece.duke.edu/people/yiran-chen/Duke Electrical & Computer Engineering
  • General AI and Cybersecurity Applications
  1. 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.

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Paper Submission Last Date
31 - December - 2025

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