Dive into real-time Computer Vision final-year projects developed using OpenCV, Python, TensorFlow, and deep learning. These projects include facial recognition, object detection, image classification, and video analysis—perfect for showcasing practical AI skills and solving real-world visual data problems.

INSTALLATION GUIDE - Vehicle Speed Detection System

Prerequisites: Python 3.8+, pip, Git

Step 1: Clone Repository
git clone <repository-url>
cd vehicle-speed-detection-New

Step 2: Create Virtual Environment
python -m venv venv
# Windows: venv\Scripts\activate
# macOS/Linux: source venv/bin/activate

Step 3: Install Dependencies
pip install -r requirements.txt
pip install django==5.0 opencv-python ultralytics easyocr numpy pandas pillow

Step 4: Download YOLOv8 Model
# Model auto-downloads on first run or manually place yolov8n.pt in root directory

Step 5: Database Setup
python manage.py migrate
python manage.py createsuperuser

Step 6: Email Configuration (Required for OTP)
# Create .env file with:
EMAIL_BACKEND=django.core.mail.backends.smtp.EmailBackend
EMAIL_HOST=smtp.gmail.com
EMAIL_PORT=587
EMAIL_USE_TLS=True
EMAIL_HOST_USER=your-email@gmail.com
EMAIL_HOST_PASSWORD=your-app-password
DEFAULT_FROM_EMAIL=your-email@gmail.com

Step 7: Run Application
python manage.py runserver
Access: http://127.0.0.1:8000/

Troubleshooting: Ensure video format is MP4, check internet connection for model download, verify email settings for OTP functionality. Processing time varies based on video length and hardware specifications.

<h2>Overview</h2>
    
    <p>Transform your final year project portfolio with this cutting-edge <strong>Vehicle Speed Detection System</strong> that combines artificial intelligence, computer vision, and web development. This unique project is perfect for final year students looking to showcase advanced Python programming skills and modern AI technologies.</p>
    
    <h3>🔥 Project Features</h3>
    <ul>
        <li><strong>Real-time Vehicle Detection:</strong> Advanced YOLOv8 model identifies cars, motorcycles, buses, and trucks with 95%+ accuracy</li>
        <li><strong>Intelligent Speed Calculation:</strong> Precise speed measurement in km/h using computer vision algorithms</li>
        <li><strong>License Plate Recognition:</strong> OCR technology extracts and reads license plate numbers automatically</li>
        <li><strong>Overspeed Alert System:</strong> Automatic flagging of vehicles exceeding speed limits (customizable threshold)</li>
        <li><strong>Video Processing Engine:</strong> Upload and process traffic videos with visual detection indicators</li>
        <li><strong>Data Export & Analytics:</strong> Generate detailed CSV reports for analysis and documentation</li>
        <li><strong>User Authentication:</strong> Secure login system with email verification and OTP validation</li>
        <li><strong>Interactive Dashboard:</strong> Modern web interface for managing processed videos and results</li>
        <li><strong>Responsive Design:</strong> Mobile-friendly interface that works on all devices</li>
        <li><strong>Progress Tracking:</strong> Real-time processing status updates for better user experience</li>
    </ul>
    
    <h3>🎯 Applications & Use Cases</h3>
    <ul>
        <li><strong>Traffic Management:</strong> Monitor and analyze traffic flow patterns in urban areas</li>
        <li><strong>Law Enforcement:</strong> Automated speed violation detection for traffic police</li>
        <li><strong>Smart City Solutions:</strong> Integration with intelligent transportation systems</li>
        <li><strong>Highway Monitoring:</strong> Continuous surveillance of vehicle speeds on highways</li>
        <li><strong>Research & Analytics:</strong> Traffic behavior analysis and pattern recognition</li>
        <li><strong>Security Systems:</strong> Vehicle identification and tracking for security purposes</li>
        <li><strong>Academic Research:</strong> Foundation for advanced computer vision and AI research projects</li>
    </ul>
    
    <h3>🏆 Why This Project Stands Out for Final Year Students</h3>
    <ul>
        <li><strong>Industry-Relevant Technology:</strong> Uses cutting-edge AI frameworks like YOLOv8 and OpenCV</li>
        <li><strong>Full-Stack Development:</strong> Combines backend AI processing with modern web frontend</li>
        <li><strong>Real-World Problem Solving:</strong> Addresses actual traffic management challenges</li>
        <li><strong>Scalable Architecture:</strong> Professional-grade system design and implementation</li>
        <li><strong>Comprehensive Documentation:</strong> Detailed setup guides and technical explanations</li>
        <li><strong>Portfolio Enhancement:</strong> Impressive project that showcases multiple technical skills</li>
        <li><strong>Career Opportunities:</strong> Opens doors to AI, computer vision, and web development roles</li>
    </ul>
    
    <h3>📊 Technical Achievements</h3>
    <ul>
        <li>Multi-object tracking algorithm with 90%+ accuracy</li>
        <li>Speed calculation precision within ±5% margin of error</li>
        <li>License plate recognition success rate of 85%+ in optimal conditions</li>
        <li>Processing speed of 15-20 FPS on standard hardware</li>
        <li>Support for multiple video formats and resolutions</li>
        <li>Automated calibration system for different camera angles</li>
    </ul>
    
    <h3>🎓 Learning Outcomes</h3>
    <p>By implementing this project, students will master:</p>
    <ul>
        <li><strong>Computer Vision:</strong> Object detection, tracking, and image processing techniques</li>
        <li><strong>Deep Learning:</strong> YOLO architecture and pre-trained model implementation</li>
        <li><strong>Web Development:</strong> Django framework, HTML/CSS, JavaScript integration</li>
        <li><strong>Database Management:</strong> User authentication, data storage, and retrieval</li>
        <li><strong>API Integration:</strong> Email services, file handling, and third-party libraries</li>
        <li><strong>System Design:</strong> Scalable architecture and performance optimization</li>
    </ul>
    
    <h3>🌟 Project Highlights</h3>
    <p>This comprehensive final year project demonstrates advanced programming concepts including:</p>
    <ul>
        <li>Multi-threading for video processing</li>
        <li>Asynchronous task handling</li>
        <li>Real-time data visualization</li>
        <li>Error handling and validation</li>
        <li>User experience optimization</li>
        <li>Security implementation</li>
        <li>Performance monitoring</li>
    </ul>
    
    <p><strong>Perfect for:</strong> Computer Science, Information Technology, Electronics & Communication Engineering students seeking unique and impressive final year projects that combine AI, web development, and practical applications.</p>

Download AI-powered Vehicle Speed Detection System with License Plate Recognition - Perfect final year college project using Python, Django, YOLOv8, OpenCV. Best unique project for computer science students. Source code available from ₹99.

AI Vehicle Speed Detection System | Final Year Python Project | codeAj

Advanced AI-driven vehicle speed detection system with license plate recognition using YOLOv8 and OpenCV. Perfect final year college project featuring computer vision, deep learning, and web development with Django framework.

Advanced AI-driven vehicle speed detection system with license plate recognition using YOLOv8 and OpenCV. Perfect final year college project featuring computer vision, deep learning, and web development.

Complete Python source code with Django framework

Source Code

Installation guide, setup instructions, and technical documentation

Project Documentation

AI-Powered Vehicle Speed Detection & License Plate Recognition System

codeAj Marketplace

1. Clone the repository: git clone https://github.com/ajayoneness/AI-Age-Detector-Pro.git  
2. Navigate to project: cd ai-age-detector-pro  
3. Create virtual environment: python -m venv age_detector_env  
4. Activate environment: 
   - Windows: age_detector_env\Scripts\activate  
   - macOS/Linux: source age_detector_env/bin/activate  
5. Install dependencies: pip install -r requirements.txt  
6. Run the app: python age_detector.py  
   - On first run, models (~200MB) will auto-download.  
   - Use --mode cli for command-line mode or --image for static images.

<h2>What is AI Age Detector Pro?</h2>
<p><strong>AI Age Detector Pro</strong> is a modern, intelligent computer vision application that uses deep learning to detect human faces and predict their age range in real-time. Whether you're a student, developer, or researcher, this project delivers professional-grade functionality with a beautiful, user-friendly interface.</p>

<h2>Key Features</h2>
<ul>
  <li><strong>Real-Time Detection:</strong> Process live webcam feed at 30+ FPS with smooth face and age detection.</li>
  <li><strong>Multiple Face Support:</strong> Detect and analyze multiple faces simultaneously.</li>
  <li><strong>Smart Age Categories:</strong> Predict age in 8 precise ranges (0–100) using a pre-trained Caffe CNN model.</li>
  <li><strong>Modern Dark GUI:</strong> Sleek, responsive Tkinter interface with blue accents, live stats, and intuitive controls.</li>
  <li><strong>Image &amp; Camera Input:</strong> Load static images (JPG, PNG, BMP) or use live camera input.</li>
  <li><strong>Export Results:</strong> Save annotated frames with timestamps and detection metadata.</li>
  <li><strong>Live Analytics:</strong> Monitor FPS, face count, and confidence scores in real time.</li>
  <li><strong>Auto-Model Download:</strong> Models download automatically on first run (~200MB).</li>
</ul>

<h2>Applications</h2>
<p>This project is ideal for:</p>
<ul>
  <li>Academic research in AI and computer vision</li>
  <li>Building age-based access systems (e.g., restricted content)</li>
  <li>Marketing analytics (audience age group detection)</li>
  <li>Student capstone or final-year projects</li>
  <li>Learning OpenCV, deep learning, and GUI development in Python</li>
  <li>Integration into larger AI surveillance or customer insight systems</li>
</ul>

<h2>Technical Architecture</h2>
<p>The system uses two powerful deep learning models:</p>
<ul>
  <li><strong>Face Detection:</strong> OpenCV’s DNN module with SSD MobileNet (300×300 input, 50% confidence threshold).</li>
  <li><strong>Age Estimation:</strong> Pre-trained Caffe CNN model trained on the IMDB-WIKI dataset across 8 age bins.</li>
</ul>
<p><strong>Performance:</strong> 30–35 FPS on 1080p input, ~85% accuracy, and under 200MB RAM usage with models loaded.</p>

<h2>Why This Project Stands Out?</h2>
<ul>
  <li><strong>No Internet Required After Setup:</strong> Fully offline capable.</li>
  <li><strong>Beginner-Friendly:</strong> Comes with detailed setup guide and comments.</li>
  <li><strong>Extensible:</strong> Easy to integrate with databases, cloud APIs, or custom models.</li>

</ul>

<h3>What You Get?</h3>
<ul>
  <li>Complete Python source code</li>
  <li>Auto-downloading model scripts</li>
  <li>Requirements.txt &amp; setup guide</li>
  <li>Well-commented, clean code structure</li>
  <li>Support for CLI and GUI modes</li>
</ul>

Get the complete Python source code for AI Age Detector Pro – a real-time age estimation system using OpenCV & deep learning. Features Tkinter GUI, live detection, and export. Starting at ₹99. Add setup, explanation, or report.

AI Age Detector Pro – Real-Time Age Prediction with GUI | Python Source Code @ ₹99 | codeAj Projects

Unlock the power of AI with AI Age Detector Pro – a real-time, deep learning-powered age estimation system built in Python using OpenCV, DNN, and Tkinter. Detect faces, estimate age groups, and export results with a sleek dark-themed GUI.

1. Extract zip to project directory\n2. Create virtual environment:\n   python -m venv venv\n   source venv/bin/activate (Linux/Mac) or venv\\Scripts\\activate (Windows)\n3. Install dependencies:\n   pip install -r requirements.txt\n4. Start server:\n   python app.py\n5. Access at http://localhost:5000

<h2>Overview</h2>
<h6>Advanced Real-Time Object Detection Solution</h6>
<p>Our <strong>Flask-powered AI application</strong> leverages state-of-the-art YOLOv8 technology to deliver lightning-fast object detection capabilities. Perfect for developers, researchers, and businesses needing intelligent video analysis solutions.</p>

<h3>Key Features</h3>
<ul>
  <li>⚡ Real-Time Webcam Processing with MJPEG Streaming</li>
  <li>📁 Video File Support (MP4, AVI, MOV, MKV)</li>
  <li>📥 Download Annotated Video Files with Detection Boxes</li>
  <li>🔍 Class Filtering for Specific Object Recognition</li>
  <li>🎨 Modern Responsive UI with Dark Mode</li>
  <li>🔄 Background Processing Queue System</li>
</ul>

<h3>Technical Specifications</h3>
<p>Built with Python's <a href="https://flask.palletsprojects.com/ ">Flask</a> framework and <a href="https://docs.ultralytics.com/ ">Ultralytics YOLOv8</a>, this application delivers production-grade performance with minimal system requirements. The MJPEG streaming architecture ensures compatibility across all modern browsers.</p>

<h3>Industry Applications</h3>
<ul>
  <li>🛡️ Security Surveillance Systems</li>
  <li>🛒 Retail Customer Analytics</li>
  <li>🚗 Traffic Monitoring Solutions</li>
  <li>🏥 Medical Imaging Analysis</li>
  <li>🤖 Industrial Automation</li>
</ul>

<h3>System Requirements</h3>
<p>Python 3.8+ environment with OpenCV and Flask dependencies. Recommended minimum hardware: 4GB RAM, 2GHz CPU. Supports GPU acceleration through CUDA-enabled OpenCV builds.</p>

Download ready-to-use real-time object detection web app built with Flask and YOLOv8. Process webcam streams and videos with class filtering. Source code available from ₹99 with setup guide and documentation.

Real-Time Object Detection Web App | YOLOv8 Flask Project with Webcam Streaming ₹99

AI-powered Flask web application with YOLOv8 for real-time object detection from webcam streams and video files. Download processed videos with bounding boxes.

1. Install Required Dependencies:
   Run `pip install -r requirements.txt`
   
   Note: The 'face_recognition' library depends on 'dlib', which requires additional tools:
   - Windows: Install Visual C++ Build Tools
   - Linux: Run `sudo apt-get install cmake build-essential`

2. Prepare Dataset:
   - Create a folder named `dataset_faces` and add student face images as `student_name.jpg`
   - Place the uniform template image as `uniform_template.jpg` inside the `input` folder

3. Directory Structure:
   .
   ├── dataset_faces/
   │   ├── john_doe.jpg
   │   └── jane_doe.jpg
   ├── input/
   │   └── uniform_template.jpg
   ├── face_uniform_attendance.py
   ├── requirements.txt
   └── attendance.csv

4. Run the Program:
   Execute `python face_uniform_attendance.py` in the terminal.

5. Press 'q' to exit the application.

<h2>Overview</h2>
<p>The <strong>Face and Uniform Based Attendance System</strong> is a next-generation solution built using advanced computer vision techniques. It ensures secure and accurate marking of attendance by verifying both facial identity and uniform compliance simultaneously.</p>

<h2>Key Features</h2>
<ul>
  <li><strong>Real-Time Face Detection & Recognition:</strong> Uses OpenCV and dlib to detect and recognize faces in live webcam feed.</li>
  <li><strong>Uniform Verification via Image Comparison:</strong> Compares captured frames with a standard uniform template using histogram matching.</li>
  <li><strong>Automatic Attendance Logging:</strong> Stores attendance records in CSV format with name, timestamp, and status.</li>
  <li><strong>Duplicate Entry Prevention:</strong> Ensures each person is marked only once per session.</li>
  <li><strong>Live Video Feed with Overlay:</strong> Shows real-time results including name, uniform status, and final outcome.</li>
  <li><strong>Multi-Face Support:</strong> Capable of detecting and processing multiple faces within a single frame.</li>
</ul>

<h2>Applications</h2>
<p>This system is ideal for use in educational institutions, corporate offices, coaching centers, or any environment where maintaining discipline through proper identification and dress code is crucial. It enhances security, eliminates manual errors, and speeds up the attendance process.</p>

<h2>Why Choose This Project?</h2>
<ul>
  <li><strong>Built with Industry-Standard Libraries:</strong> Developed using Python, OpenCV, NumPy, Pandas, and face_recognition for high accuracy.</li>
  <li><strong>Customizable Thresholds:</strong> Adjust face and uniform comparison thresholds based on lighting and environmental conditions.</li>
  <li><strong>Easy Integration:</strong> Can be easily integrated into existing systems without requiring expensive hardware.</li>
  <li><strong>Cost-Effective:</strong> Eliminates the need for traditional biometric devices like fingerprint scanners.</li>
</ul>

<h2>Who Should Buy This?</h2>
<p>This project is perfect for students working on AI/ML-based final year projects, developers building smart campus solutions, startups aiming for tech-driven education tools, or organizations looking to digitize their attendance system securely.</p>

<h2>Project Benefits</h2>
<ul>
  <li>Highly accurate facial recognition using deep learning models</li>
  <li>Ensures proper adherence to uniform standards</li>
  <li>Logs attendance data in clean, exportable CSV format</li>
  <li>Supports batch processing and real-time monitoring</li>
  <li>Lightweight and easy to deploy on any Windows/Linux machine</li>
</ul>

<h2>Buy Now from codeAj Marketplace</h2>
<p>Get the complete source code starting at just ₹99. You can also opt for additional services like <strong>Project Setup</strong>, <strong>Source Code Explanation</strong>, and a ready-to-submit <strong>Project Report</strong>.</p>

Download the Face and Uniform Based Attendance System with real-time face recognition and uniform verification. Get the full source code starting at ₹99 only at codeAj Marketplace.

Face & Uniform Based Attendance System | AI Project Source Code | codeAj Marketplace

A smart attendance system that uses computer vision for face recognition and uniform verification to mark attendance in real-time. Ideal for schools, colleges, and offices.

An AI-powered attendance system that uses face recognition and uniform verification to mark attendance in real-time.

Face and Uniform Based Attendance System

Installation Steps
Install Python 3.8 or Higher
Ensure you have Python installed on your system.
Verify installation with:
python --version

Install Required Dependencies
pip install -r requirements.txt
This installs essential packages such as opencv-python, torch, flask, and numpy.
Download YOLOv8 Weights
The weights will be downloaded automatically upon first run of main.py.
Alternatively, you can manually download them from the official Ultralytics repository.

Run the Main Application
python main.py

Optional: Start the Web Dashboard
python dashboard.py

Access the Dashboard
Open a browser and navigate to:

http://localhost:5000

<h2>Overview</h2>
<p>The <strong>Classroom Cheat Detection System</strong> is a cutting-edge solution designed to maintain academic integrity by detecting suspicious behavior during exams using real-time video analytics.</p>

<h3>Key Features</h3>
<ul>
    <li><strong>Real-Time Head Orientation Detection:</strong> Monitors student head movements to identify signs of cheating.</li>
    <li><strong>Mobile Phone Detection:</strong> Detects unauthorized phone usage during examinations using YOLOv8 model.</li>
    <li><strong>Student Tracking & Movement Monitoring:</strong> Tracks movement patterns to flag unusual behavior.</li>
    <li><strong>Suspicious Behavior Alerts:</strong> Sends instant alerts when potential cheating is detected.</li>
    <li><strong>Log and Snapshot Storage:</strong> Automatically captures snapshots and logs incidents for later review.</li>
    <li><strong>Web Dashboard:</strong> User-friendly interface to monitor live feeds and access recorded events.</li>
</ul>

<h3>Applications</h3>
<ul>
    <li>Academic institutions for maintaining exam integrity.</li>
    <li>Online proctoring systems with physical classroom integration.</li>
    <li>Behavioral analysis tools for educational research.</li>
    <li>Surveillance systems in controlled environments.</li>
</ul>

<h3>Why Choose This System?</h3>
<p>This system leverages modern computer vision techniques and machine learning models to offer an automated, scalable, and accurate approach to monitoring classrooms without human supervision. It’s ideal for universities, colleges, and competitive exam centers aiming to prevent malpractice.</p>

AI-powered Classroom Cheating Detection System using Computer Vision and YOLOv8 for real-time surveillance and alert generation.

Smart Classroom Surveillance System – Real-Time Cheating Detection with AI

An advanced AI-driven system that detects cheating behaviors in real-time using computer vision and YOVA-based object detection.

<h2>Face Recognition and Attendance System</h2>
<p>The <strong>Face Recognition and Attendance System</strong> is an innovative project that integrates facial recognition technology with an automated attendance system. This system uses a webcam to detect individuals' faces and automatically records their attendance in an Excel sheet, making the entire process seamless and highly efficient. This system can be employed in educational institutions, workplaces, and other environments where attendance management is necessary.</p>

<h3>Project Features:</h3>
<ul>
    <li><strong>Real-Time Face Detection:</strong> Uses a webcam to capture and detect faces in real-time.</li>
    <li><strong>Automatic Attendance Tracking:</strong> Records attendance automatically in an Excel sheet without manual intervention.</li>
    <li><strong>High Accuracy:</strong> Utilizes advanced facial recognition algorithms for high accuracy in detection.</li>
    <li><strong>User-Friendly Interface:</strong> Easy to use with minimal setup required.</li>
    <li><strong>Customizable:</strong> The system can be tailored to the needs of educational institutions, offices, or any attendance-based system.</li>
</ul>

<h3>Applications:</h3>
<ul>
    <li><strong>Educational Institutions:</strong> Perfect for managing student attendance in schools and colleges.</li>
    <li><strong>Workplaces:</strong> Efficient attendance tracking for employees in any company.</li>
    <li><strong>Events:</strong> Ideal for event management, where attendance needs to be tracked for participants.</li>
    <li><strong>Smart Homes/Offices:</strong> Can be integrated into smart home or office systems for added convenience and security.</li>
</ul>

<h3>How It Works:</h3>
<p>The system captures the image of individuals through a webcam. It then compares the detected face with a pre-existing database to verify identity and records the attendance if the match is successful. The attendance data is then automatically saved into an Excel file, providing a hassle-free and paperless solution for attendance management.</p>

<h3>Technology Used:</h3>
<ul>
    <li>Python</li>
    <li>OpenCV</li>
    <li>Face Recognition </li>
    <li>Excel File Handling (Pandas)</li>
</ul>

Discover the Face Recognition and Attendance System project, which uses webcam technology to detect faces and automatically records attendance in an Excel sheet. Perfect for schools, offices, and events.

Face Recognition and Attendance System | Real-Time Attendance Tracking

A real-time attendance system that uses facial recognition to detect faces via a webcam and records attendance automatically in an Excel sheet.

CodeAJ

A real-time face recognition system that uses a webcam to detect faces and record attendance in an Excel sheet, designed for schools, offices, and event management.

Computer vision Projects

AI-Powered Vehicle Speed Detection & License Plate Recognition System - Final Year Python Project

AI Age Detector Pro – Real-Time Age Prediction with Smart GUI | Source Code

Real-Time Object Detection Web App Using YOLOv8 & Flask | AI-Powered Webcam & Video Processing

AI-Powered Face & Uniform-Based Attendance System with Real-Time Recognition

Smart Classroom Surveillance: AI-Powered Cheating Detection System

Face Recognition and Attendance Project