Speed Estimation of Vehicles using YOLOv8

Overview

This project utilizes YOLOv8 for object detection and tracking to estimate the speed of vehicles in real-time. The system identifies and tracks vehicles across frames and calculates their speed based on the displacement over time.

Features

• Vehicle Detection: Identifies various types of vehicles (cars, trucks, motorcycles, buses, etc.).
• Object Tracking: Tracks vehicles across frames to maintain continuity.
• Speed Estimation: Computes vehicle speed based on frame rate and pixel displacement.
• Real-time Processing: Optimized for high FPS tracking and inference.
• Demo Video: A sample video demonstrating the results is included.

Technology Stack

• YOLOv8: Object detection and tracking
• DeepSORT: Multi-object tracking
• OpenCV: Video processing
• Python: Main programming language
• NumPy & Pandas: Data processing

Installation

# Clone the repository
git clone https://github.com/MoizAhmed2517/Speed-Estimation.git
cd Speed-Estimation

# Install dependencies
pip install -r requirements.txt

Results

The model successfully detects and tracks vehicles, estimating their speed based on movement across frames. A sample output video is included in the repository.

Video Demonstration

Click the play button above to watch the demo.

Contributing

Feel free to fork the repository and open pull requests for improvements!

License

This project is licensed under the MIT License.

Contact

For queries, reach out via [email protected] or create an issue in the repository.

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YOLOv8 speed estimation, vehicle tracking, real-time object tracking, AI-based traffic monitoring, deep learning for traffic analysis, computer vision speed detection, real-time vehicle speed tracking.