Authors :
Sadheen Hossain; Aous Shaheen; Dr. B.S. Satpute; Tariqul Islam Sani
Volume/Issue :
Volume 9 - 2024, Issue 6 - June
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.
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 :
- Bradski G., & Kaehler A. (2008). Learning OpenCV: Computer vision with the OpenCV library. O'Reilly Media, Inc.
- Li H., Li Y., Li B., & Zou Y. (2019). Vehicle detection and tracking in a multi-camera system. Journal of Visual Communication and Image Representation.
- 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.
- Guo Y., Liu Y., Ouyang W., & Li X. (2019). Deep learning for pedestrian detection: A comprehensive review. Neurocomputing.
- Ghosal S., Verma Y., & Mehrotra S. (2018). A survey on object detection and tracking in video surveillance. Journal of Ambient Intelligence and Humanized Computing.
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.