Real-Time Object Detection and Tracking

CodeAlpha Internship Project

This project was developed as part of the CodeAlpha Artificial Intelligence Internship program.

Project Description

The objective of this project is to perform real-time object detection and tracking using computer vision techniques. The system processes video streams from a webcam or video file, detects objects using a pre-trained YOLOv8 model, and tracks them across frames using the Deep SORT tracking algorithm.

Features

• Real-time video capture using OpenCV
• Object detection using YOLOv8
• Bounding box visualization with class labels
• Multi-object tracking with unique tracking IDs
• Support for webcam and video file input
• Real-time display of tracked objects

Technologies Used

• Python
• OpenCV
• YOLOv8 (Ultralytics)
• Deep SORT
• NumPy
• PyTorch

Project Structure

object_detection_tracking/
│
├── main.py
├── requirements.txt
├── README.md
├── videos/
│   └── sample.mp4
└── models/
    └── yolov8n.pt

Installation

Clone the Repository

git clone https://github.com/your-username/object-detection-tracking.git
cd object-detection-tracking

Install Dependencies

pip install -r requirements.txt

How to Run

Webcam Input

python main.py

Video File Input

Replace the source path in main.py with your video file and run:

python main.py

Expected Output

The system displays:

• Detected object names
• Confidence scores
• Bounding boxes
• Unique tracking IDs

Example:

Person ID: 1
Car ID: 2
Bicycle ID: 3

Applications

• Smart Surveillance Systems
• Traffic Monitoring
• Security and Safety Analytics
• Crowd Management
• Autonomous Systems

Internship Information

Internship: CodeAlpha Artificial Intelligence Internship

Task: Task 4 – Object Detection and Tracking

Domain: Artificial Intelligence & Computer Vision

Author

Amulya Balaji

CodeAlpha AI Intern