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@article{180598,
        author = {Gaurav Kamble and Ashwini Taskal and Mrunal Gaikwad and Shrddha Jagtap and Ferzin Patel and Pournima Parase},
        title = {VEHICLE THEFT DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2370-2375},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180598},
        abstract = {The rise in vehicle theft cases emphasizes the 
urgent need for innovative and effective security 
mechanisms. Conventional security systems, such as 
manual surveillance, CCTV monitoring, and GPS 
tracking, often fall short due to their delayed response 
time and vulnerability to hacking or environmental 
factors. To address these challenges, this research 
presents a Vehicle Theft Detection System (VTDS) that 
integrates artificial intelligence and computer vision for 
real-time monitoring and detection. The proposed VTDS 
employs the Haar Cascade Classifier, a machine 
learning-based object detection method, to recognize 
authorized and unauthorized vehicles. By utilizing Open 
CV for live video analysis, the system enhances security 
by instantly identifying potential threats and triggering 
immediate alerts via SMS, email, or alarm notifications. 
The system not only automates vehicle surveillance but 
also minimizes false alarms and human intervention, 
making it a cost effective alternative to traditional security 
measures. Furthermore, IoT integration ensures 
seamless remote monitoring, enabling vehicle owners to 
track security breaches in real time. Keyword- Haar 
Cascade Classifier, Real-Time Monitoring, Open CV, 
Machine Learning, Real-Time Monitoring, Theft 
Prevention.},
        keywords = {},
        month = {June},
        }