---
title: "Object Detection in Live YouTube Streams"
emoji: 🎥
colorFrom: blue
colorTo: green
sdk: gradio
python_version: "3.8"
sdk_version: "4.7.1"
app_file: "app.py"
tags:
  - live-video
  - object-detection
  - YOLO
  - YouTube
  - gradio
models:
  - yolov8x
datasets:
  - HuggingFaceM4/COCO
---
# Object Detection in Live YouTube Streams

## Project Status: Active

## Installation

To use and install this project, follow these steps:

1. Clone the repository from GitHub.
2. Ensure Python 3.8 or higher is installed on your machine.
3. Install required dependencies using `pip install -r requirements.txt`.
4. Run `python3 app.py` file to start the application.

## Objective

The primary goal of this project is to harness computer vision and machine learning technologies for real-time, accurate object detection in live YouTube streams. By focusing on this, we aim to unlock new potential in areas critical to modern society, such as enhanced security surveillance, efficient urban management, and advanced traffic analysis systems. Our objective is to develop a robust system that not only identifies and classifies objects in diverse streaming environments but also adapts to varying conditions with high precision and reliability.

## Contributors

- **William Acuna**
- **Jonathan Agustin**
- **Alec Anderson**

## Methods Used

- **Computer Vision and Object Detection**: Computer vision techniques and object detection models identified and classified objects in live video feeds.
- **Machine Learning and Deep Learning**: Machine learning, especially deep learning, interpreted complex visual data from video streams.
- **Data Streaming**: Efficient data streaming methods handled the live video feeds from online sources.
- **User Interface Design**: A user-friendly interface enabled simple interaction with the system, including video input and result visualization.
- **API Integration for Video Retrieval**: API solutions retrieved of live video content from popular online platforms.

## Technologies

- **Python**: Primary programming language for the project's development.
- **Git**: Version Control System for tracking and managing changes in the codebase.
- **GitHub**: Platform for code hosting, collaboration, and version control.
- **YouTube API**: Data source for accessing live YouTube streams.
- **Ultralytics (YOLOv8)**: Object detection model for real-time video analysis.
- **Google Colab**: Cloud-based platform for development and testing of the model.
- **Hugging Face Spaces**: Deployment service for hosting the machine learning model.
- **Gradio**: Framework for building the user interface and facilitating user interactions.

## Project Description

This project is centered around the creation and deployment of a sophisticated object detection system, specifically tailored for live YouTube streams. Utilizing the advanced capabilities of the Ultralytics YOLO model, this system is designed to identify, classify, and track objects in real-time within dynamic streaming environments. A key aspect of our endeavor is to address and overcome challenges associated with variable lighting conditions, object occlusions, and diverse environmental settings, ensuring the system's effectiveness and accuracy in real-world applications. Moreover, we aim to optimize the system for speed and efficiency, ensuring minimal latency in real-time processing. The project not only represents a significant advancement in computer vision but also offers a versatile tool with wide-ranging applications, from urban planning and public safety to traffic management and surveillance.

## License

This project is licensed under the MIT License - see the LICENSE.md file for details.

## Acknowledgments

- **Professor Roozbeh Sadeghian**: Our advisor, for invaluable guidance and mentorship.
- **Professor Ebrahim Tarshizi**: The Academic Director for the Applied Artificial Intelligence (AAI) program, for contributions to program structure and academic enrichment.
- **The Applied Artificial Intelligence Program at the University of San Diego**: For essential support and resources.