🎯 Tracko - Real-Time Object Tracking Application

📖 Overview

Tracko is a powerful, cutting-edge mobile application that performs real-time object detection and tracking using advanced AI technology. Built with Flutter for the mobile frontend and Python FastAPI for the backend, Tracko combines state-of-the-art YOLO (You Only Look Once) deep learning models with an intuitive user interface to track objects in real-time through your device camera.

🎓 Why This App Was Made

Problem Statement

Traditional object recognition systems are either:

Too complex - Requiring technical expertise to operate
Not real-time - Processing videos after recording
Limited features - Only detecting without tracking or analytics
Platform-specific - No cross-platform support

Solution: Tracko

Tracko was created to provide:

Accessibility - Anyone can use professional-grade object tracking
Real-time performance - Instant detection and tracking at 10-12 FPS
Comprehensive features - Detection + tracking + analytics + recording
Cross-platform - Works on Android/iOS with one codebase
Multilingual - Supports English, Urdu, and Sindhi

💡 Why This App Is Useful

Real-World Applications

1. Security & Surveillance

Monitor public spaces for specific objects
Count people entering/exiting areas
Track abandoned objects
Detect unauthorized items

2. Retail & Business

Customer traffic analysis (footfall counting)
Product placement optimization
Queue management
Inventory monitoring

3. Smart Cities

Traffic flow analysis
Vehicle counting and classification
Pedestrian safety monitoring
Urban planning data collection

4. Research & Education

Wildlife behavior studies
Sports performance analysis
Classroom activity monitoring
Laboratory experiment tracking

5. Personal Use

Pet monitoring and activity tracking
Home security
Package delivery detection
Parking space monitoring

🛠️ Technology Stack

Frontend: Flutter

What is Flutter?

Google's UI toolkit for building natively compiled applications
Single codebase for iOS, Android, web, and desktop
Fast development with hot reload
Beautiful, customizable UI components

Why Flutter?

✅ Cross-platform - One code for all platforms
✅ Performance - Compiled to native code (60+ FPS)
✅ Rich UI - Material Design & Cupertino widgets
✅ Fast development - Hot reload in milliseconds
✅ Strong ecosystem - 25,000+ packages

Flutter Packages Used:

camera - Camera access and preview
http - API communication
provider - State management
permission_handler - Runtime permissions
path_provider - File system access
flutter_localizations - Multi-language support

Backend: Python FastAPI

What is FastAPI?

Modern, fast web framework for building APIs with Python
Automatic API documentation (Swagger UI)
Type hints and validation
High performance (comparable to NodeJS and Go)

Why FastAPI?

✅ Fast - One of the fastest Python frameworks
✅ Easy to use - Intuitive and Pythonic
✅ Automatic docs - Interactive API documentation
✅ Type safety - Built-in validation
✅ Async support - Non-blocking operations

Python Libraries Used:

ultralytics - YOLOv8 implementation
opencv-python - Image processing
torch (PyTorch) - Deep learning framework
numpy - Numerical computations
pillow - Image handling

AI Engine: YOLOv8

What is YOLO?

"You Only Look Once" - Real-time object detection algorithm
Latest version: YOLOv8 (2023)
State-of-the-art accuracy and speed
Detects 80 different object classes (COCO dataset)

Why YOLOv8?

✅ Real-time - Processes 30-60 frames per second
✅ Accurate - 50%+ mean Average Precision (mAP)
✅ Versatile - Detects people, vehicles, animals, objects
✅ Efficient - Runs on CPU (no GPU required)
✅ Pre-trained - Ready to use out of the box

Model Variants:

yolov8n - Nano (fastest, 3MB)
yolov8s - Small (balanced)
yolov8m - Medium
yolov8l - Large
yolov8x - Extra-large (most accurate)

🌟 What This App Can Do

Core Features

1. Real-Time Object Detection 🎯

Detects 75+ object types instantly
Live bounding boxes with labels
Confidence scores (accuracy percentage)
Distance estimation for detected objects

2. Object Tracking 📍

Assigns unique IDs to objects
Tracks movement across frames
Maintains tracking even with occlusion
Speed calculation (pixels/second)

3. Screenshot Capture 📸

Capture frames with detection overlays
Save to device storage
Automatic timestamping
Quick access via floating button

4. Session History 📋

Records all tracking sessions
Shows total detections, duration, FPS
Object breakdown per session
Persistent local storage

5. Data Export 💾

Export to JSON (detailed data)
Export to CSV (spreadsheet format)
Includes timestamps, object counts
Ready for data analysis

6. Analytics & Insights 📊

Total detection counter
Most frequently detected objects
Average speed calculations
Dwell time (how long objects stay)
Confidence statistics

7. Multilingual Support 🌍

English (International)
Urdu (اردو)
Sindhi (سنڌي)
Easy language switching

8. Performance Optimizations ⚡

10-12 FPS real-time tracking
80ms processing interval
Optimized camera resolution
Efficient battery usage

9. Customization 🎨

Flash control (Off/On/Auto)
Camera switching (front/back)
Transparent UI elements
Adjustable display settings

🆚 Tracko vs Other Applications

Advantages Over Competitors

Feature	Tracko	Google Lens	Other Apps
Real-time tracking	✅ Yes (10-12 FPS)	❌ No (single image)	⚠️ Some
Offline mode	✅ Local processing	❌ Requires internet	❌ Cloud only
Multi-language	✅ 3 languages	✅ 100+ languages	⚠️ Limited
Analytics	✅ Comprehensive	❌ No	❌ Basic
Session history	✅ Full tracking	❌ No	❌ Limited
Data export	✅ JSON + CSV	❌ No	❌ No
Free & open	✅ Completely free	⚠️ Free but data collected	💰 Paid
Privacy	✅ All local	❌ Cloud processing	❌ Data uploaded
Custom models	✅ Switchable	❌ Fixed	❌ Fixed
Distance calc	✅ Yes	❌ No	❌ No

What Makes Tracko Unique

1. Complete Local Processing

No internet required after setup
All data stays on your device
No privacy concerns
No data collection

2. Professional-Grade Features

Session management
Detailed analytics
Data export capabilities
Speed and dwell time metrics

3. Built for Extension

Modular architecture
Easy to add new features
Customizable models
Developer-friendly

4. Educational Value

Learn about AI/ML
Understand object detection
Practical computer vision examples
Open source code

📊 Technical Specifications

Performance Metrics

FPS: 10-12 frames per second
Latency: 80ms per frame
Accuracy: 50-70% mAP (model dependent)
Objects: 75+ types supported
Distance: Estimated via focal length calculation

System Requirements

Mobile Device:

Android 6.0+ or iOS 11.0+
2GB RAM minimum (3GB+ recommended)
Camera (any resolution)
100MB free storage

Backend Server:

Python 3.10+
2GB RAM (4GB+ recommended)
CPU (GPU optional for faster processing)
~500MB for dependencies

🏗️ Architecture

System Overview

┌─────────────────┐      HTTP/REST     ┌──────────────────┐
│                 │ ◄─────────────────► │                  │
│  Flutter App    │   80ms intervals   │  FastAPI Server  │
│  (Frontend)     │   Image + Response │  (Backend)       │
│                 │                     │                  │
└────────┬────────┘                     └────────┬─────────┘
         │                                       │
         │ Camera Feed                           │ Processing
         │ User Input                            │ YOLO Model
         │ Display                               │ Detection
         │                                       │
         ▼                                       ▼
   ┌──────────┐                           ┌──────────┐
   │  Device  │                           │ YOLOv8   │
   │  Camera  │                           │  Model   │
   └──────────┘                           └──────────┘

Data Flow

Camera captures frame
Frontend sends image to backend
Backend processes with YOLO
Model detects objects and returns coordinates
Backend sends detection data
Frontend displays bounding boxes
Analytics update in real-time

📁 Project Structure

tracko/
├── lib/                              # Flutter frontend
│   ├── features/
│   │   └── object_tracking/
│   │       ├── controllers/         # Business logic
│   │       ├── models/              # Data structures
│   │       ├── screens/             # UI screens
│   │       ├── widgets/             # Reusable components
│   │       ├── services/            # API & storage
│   │       └── utils/               # Helper functions
│   ├── core/
│   │   ├── constants/               # Colors, themes
│   │   ├── services/                # Global services
│   │   └── utils/                   # Localization
│   └── common/
│       └── widgets/                 # Shared widgets
├── backend/                          # Python backend
│   ├── main.py                      # FastAPI server
│   ├── requirements.txt             # Dependencies
│   └── yolov8n.pt                   # YOLO model
└── assets/
    └── translations/                # Language files

🚀 Getting Started

Prerequisites

# Install Flutter
flutter --version # Should be 3.10.0+

# Install Python
python3 --version # Should be 3.10+

Backend Setup

cd backend
pip3 install -r requirements.txt
python3 -m uvicorn main:app --host 0.0.0.0 --port 8000 --reload

Frontend Setup

flutter pub get
flutter run

📈 Future Enhancements

Planned Features:

🤝 Contributing

Tracko is designed to be educational and extensible. Contributions are welcome!

Areas for contribution:

New detection models
UI improvements
Performance optimizations
Additional languages
Bug fixes

📄 License

This project is for educational purposes. The YOLO model follows Ultralytics AGPL-3.0 License.

👨‍💻 Developer

Built with ❤️ using Flutter & FastAPI

Technologies:

Frontend: Flutter 3.10+
Backend: Python 3.10+ with FastAPI
AI: YOLOv8 by Ultralytics
State Management: Provider
Languages: Dart, Python

📞 Support

For issues, questions, or feature requests, please refer to the project documentation.

Tracko - Making professional object tracking accessible to everyone! 🎯

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
android		android
assets/translations		assets/translations
backend		backend
ios		ios
lib		lib
linux		linux
macos		macos
test		test
web		web
windows		windows
.gitignore		.gitignore
.metadata		.metadata
README.md		README.md
analysis_options.yaml		analysis_options.yaml
documentation.md		documentation.md
flutter_01.png		flutter_01.png
flutter_02.png		flutter_02.png
flutter_03.png		flutter_03.png
flutter_04.png		flutter_04.png
flutter_05.png		flutter_05.png
instruction.md		instruction.md
pubspec.lock		pubspec.lock
pubspec.yaml		pubspec.yaml
runpython.sh		runpython.sh

Imtiaz-khoso/tracko_python_flutter

Folders and files

Latest commit

History

Repository files navigation