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@article{176583,
        author = {Aadya and Arpit Mohan and Tanisha Rikhi and Neha Ahlawat},
        title = {VEHICLE FEATURE DETECTION USING IMAGE PROCESSING AND OPENCV},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7319-7330},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176583},
        abstract = {The increasing need for cost-effective and accurate vehicle monitoring systems has led to the development of alternatives to traditional radar-based methods. This paper presents a Python-based Vehicle Feature Detection System (VFDS) that utilizes a single-camera setup to capture live or recorded video and extract key vehicle attributes such as speed, color, and count. The system employs open-source libraries including OpenCV and TensorFlow to implement image processing and object tracking techniques. Results show that VFDS delivers reliable performance in estimating vehicle speed and features, offering a more affordable solution compared to conventional radar systems. This research emphasizes the importance of camera calibration, geometric modeling, and real-time processing in achieving accurate detection, aiming to provide a modular, scalable, and accessible approach for traffic monitoring applications.},
        keywords = {Vehicle Feature Detection, Speed Estimation, Image Processing, Object Tracking, OpenCV, TensorFlow, Single- Camera System, Traffic Monitoring},
        month = {April},
        }