Authors : Sadheen Hossain; Aous Shaheen; Dr. B.S. Satpute; Tariqul Islam Sani

Volume/Issue : Volume 9 - 2024, Issue 6 - June

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Google Scholar : https://tinyurl.com/5n9bbzyv

Scribd : https://tinyurl.com/2w4xbrrn

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN1260

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : This project focuses on developing a vehicle detection system using OpenCV, a real-time computer visionlibrary in Python. The project aims to create a vehicle counting and detection system that works effectively forvideos using OpenCV for image processing. The system will utilize OpenCV's computer vision capabilities to identifyvehicles and count the number of vehicles along with addinga unique id for each vehicle in the video. The system has potential applications in traffic monitoring, parking management, and transportation planning. The results of the project demonstrate the capabilities of OpenCV in creating efficient and accurate vehicle detection and classification systems.

Keywords : Vehicle Detection, Object Detection, Opencv, Image Processing, Computer Vision.

References :

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3. Khan F. S., Anwer R. M., & van de Weijer J. (2016). Object detection in videos with tubelet proposal networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
4. Guo Y., Liu Y., Ouyang W., & Li X. (2019). Deep learning for pedestrian detection: A comprehensive review. Neurocomputing.
5. Ghosal S., Verma Y., & Mehrotra S. (2018). A survey on object detection and tracking in video surveillance. Journal of Ambient Intelligence and Humanized Computing.