Real-time Object Detection and Tracking with YOLOv8 & Streamlit

Welcome to Begum Rokeya University, Rangpur

Team Leader: Md. An Nahian Prince Dept: CSE,BRUR ID: 12105007

This repository is an extensive open-source project showcasing the seamless integration of object detection and tracking using YOLOv8 (object detection algorithm), along with Streamlit (a popular Python web application framework for creating interactive web apps). The project offers a user-friendly and customizable interface designed to detect and track objects in real-time video streams from sources such as RTSP, UDP, and YouTube URLs, as well as static videos and images.

Explore Implementation Details on Medium (3 parts blog series)

WebApp Demo on Streamlit Server

Note: In the demo, Due to non-availability of GPUs, you may encounter slow video inferencing.

Tracking With Object Detection Demo

Demo Pics

Requirements

• Python 3.6+
• YOLOv8
• Streamlit

Usage

• Run the app with the following command: streamlit run app.py
• The app should open in a new browser window.

Machine Learning Model Configuration

• Select task: Choose between "Detection" and "Segmentation".
• Select model confidence: Use the slider to adjust the confidence threshold (1-100) for the model.

Once the model configuration is done, select a source for the input.

Detection on images

• The default image with its objects-detected image is displayed on the main page.
• Select a source: Choose "Image" from the radio button selection.
• Upload an image: Click on the "Browse files" button to upload an image from your local machine.
• Click the "Detect Objects" button: This will run the object detection algorithm on the uploaded image with the selected confidence threshold.
• Resulting image: The resulting image with objects detected will be displayed on the page. Click the "Download Image" button to download the image.("If save image to download" is selected)

Detection in Videos

• Create a folder named "videos" in the same directory as your app.
• Place your video files in the "videos" folder.
• Edit settings.py:
  • Modify the VIDEO_DIR variable: This variable should point to the newly created "videos" folder.
  • Edit the VIDEO_1_PATH, VIDEO_2_PATH, etc. variables: Use the exact names of your video files (e.g., "video_1.mp4", "video_2.mp4").
  • Update the VIDEOS_DICT dictionary: Ensure the names used in the dictionary match the names of your video files.
• Select the source as "Video".
• Choose a video from the dropdown menu: The videos you placed in the "videos" folder will appear here.
• Click the "Detect Video Objects" button: The selected task (detection/segmentation) will start on the chosen video.

Detection on YouTube Video URL

• Select the source as "YouTube".
• Paste the YouTube video URL into the text box.
• Click the "Detect Video Objects" button: The detection/segmentation task will start on the provided YouTube video URL.

Acknowledgements

This app uses YOLOv8 for object detection algorithms and Streamlit library for the user interface.

Disclaimer

This project is intended as a learning exercise and demonstration of integrating various technologies, including:

• Streamlit
• YoloV8
• Object-Detection on Images And Live Video Streams
• Python-OpenCV

Please note that this application is not designed or tested for production use. It serves as an educational resource and a showcase of technology integration rather than a production-ready web application.

Contributors and users are welcome to explore, learn from, and build upon this project for educational purposes.

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