This repository contains code for a vehicle tracking model developed using OpenCV and YOLOv8 which supports a range of vision AI tasks, including detection, segmentation, tracking, and classification. This project aims to track various objects throughout a video and determine the number of vehicles leaving and entering the frame. This code serves as a preliminary implementation for detecting traffic congestion. It sets up the necessary parameters and initial configurations for analyzing traffic patterns.
To run this project in Google Colab, click the "Open in Colab" button above. Follow these simple steps to set it up:
- Click the "Open in Colab" button above to open the notebook in Google Colab.
- Run the setup cell: The first cell installs all necessary libraries and mounts Google Drive if needed. You only need to do this once.
- Follow the notebook instructions: Run each cell in order to execute the code and see the results.
Note: You must have a Google account to use Google Colab.
- Google Account to access Google Colab.
- Python dependencies specified in the
requirements.txtfile (automatically installed in the setup cell).
Below is an example of a processed frame from the video. (Sample video source: https://www.pexels.com/video/traffic-flow-in-the-highway-2103099/)
You can change and experiment with other parameters supported by YOLOv8 to build upon this project using different datasets.