EdgeAI: Vehicle Tracking & Visualization

• Berat Emir Mutlu
• Kazim Efe Koçyiğit

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📌 Overview

This project performs real-time vehicle detection and tracking on video input using YOLOv8.
It includes:

• A vehicle tracking pipeline in Python (OpenCV + YOLOv8).
• A web-based visualization interface to display results.

The code is designed to be extendable for color detection, direction detection, and possible Federated Learning integration.

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🧠 Current Features

Vehicle Detection & Tracking

• YOLOv8 for detecting vehicles in video frames.
• Tracking across frames using tracker.py + SORT algorithm.
• Real-time display with bounding boxes and labels.

Web Visualization

• Static HTML/CSS/JS interface to load and display prediction data from .json files.
• Data table & chart visualization (requires manually prepared JSON).

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🚀 Quick Start

1) Requirements

• Python ≥ 3.8
• Packages:
  opencv-python, numpy, ultralytics
• Browser: Any modern browser (Chrome/Firefox/Edge)
• VS Code with Live Server extension (for visualization)

Install Python dependencies:

python -m venv .venv
# Windows: .venv\Scripts\activate
# macOS/Linux: source .venv/bin/activate
pip install -U pip
pip install opencv-python numpy ultralytics

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2) Project Structure

EdgeAI-Visual-Perception/
│
├─ Specialization/                # Core detection/tracking pipeline
│  ├─ calcDirection.py             # direction detection
│  ├─ cropVehicle.py               # Extracts vehicle ROIs
│  ├─ detectColor.py               # color detection
│  ├─ detector.py                  # YOLO detection logic
│  ├─ main.py                      # Entry point
│  ├─ renderer.py                  # Renders labels/boxes on frames
│  ├─ sort.py                      # SORT tracking implementation
│  ├─ tracker.py                   # Tracker initialization/update
│  ├─ utils.py                     # Utility functions
│  ├─ vidoe2.webm                  # Sample video 
│  └─ yolov8n.pt                   # YOLOv8 weights
│
├─ docs/                           # Web-based visualization
│  ├─ assets/                       # Images/icons/etc.
│  ├─ data.json                     # Example data file
│  ├─ index.html                    # Main HTML page
│  ├─ script.js                     # Logic for loading/displaying data
│  └─ style.css                     # Styling for visualization
│
├─ Documentation.pdf                # Additional documentation
└─ README.md

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▶️ Run the Vehicle Tracker

Currently, the only supported arguments are:

python main.py --video path/to/video --model path/to/yolov8.pt

Arguments

• --video — Path to a video file (e.g., sample.mp4).
  Default: "vidoe2.webm" (rename if needed).
• --model — Path to YOLOv8 model weights. Default: yolov8n.pt.

Examples

# Use sample video
python main.py --video vidoe2.webm --model yolov8n.pt

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📊 Visualize Results (Web GUI)

Steps (VS Code Live Server)

1. Open Visualisation/ in VS Code.
2. Install the Live Server extension.
3. Right-click index.html → Open with Live Server.
4. Double-click index.html if browser did not launch automatically.

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