Traffic Density Detection with Count using Python with Deep Learning | Python Project |
In this video, I demonstrate a project that uses Python and deep learning techniques to detect traffic density in a video feed. The project leverages OpenCV and a pre-trained SSD MobileNet model to detect vehicles in real-time and calculate traffic density. Watch the video to see how the algorithm identifies vehicles such as cars, trucks, and motorcycles, and provides a count and traffic density measurement.
Links :-
- Instagram: [ / arhan.mansoori.9484 ]
- LinkedIn: [ / arhan-mansoori-8983791a8 ]
- GitHub Source Code: [https://github.com/Arhanmansoori/Traf...]
- If the GitHub link does not work, please check the comment section for an updated link.
Code Overview:-
- Object Detection : Utilizes the pre-trained SSD MobileNet v3 model for real-time detection of vehicles in the video.
- Vehicle Count : Counts the number of vehicles such as bicycles, cars, motorcycles, and trucks.
- Traffic Density : Estimates traffic density based on the number of vehicles detected.
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