AIR-CANVAS: A Computer Vision Based Gesture Drawing System

Abstract

The Air Canvas Project is an innovative computer vision application developed using OpenCV and Python, which enables users to draw in the air using simple hand gestures tracked via a webcam. By utilizing a coloured marker—typically placed at the tip of a finger—the system captures the motion of the marker in real time and renders corresponding strokes on a virtual canvas. The project leverages key techniques in computer vision, including colour detection in HSV colour space, contour tracking, and morphological operations like erosion and dilation to enhance accuracy and reduce noise. The system continuously tracks the position of the coloured marker, stores the coordinates, and draws the path traced by the user across successive video frames. This approach eliminates the need for traditional input devices, offering a touchless, interactive experience. Designed with accessibility and simplicity in mind, the Air Canvas can serve as both an educational tool for learning computer vision and a creative platform for digital expression. The project demonstrates the potential of combining real-time image processing and gesture recognition to create intuitive, user-friendly applications.

Introduction

I. Project Overview

The Air Canvas Project is an interactive computer vision application that allows users to draw in the air using hand gestures and a coloured marker placed at their fingertip. A webcam captures the motion, and the system converts it into real-time digital drawings without requiring physical input devices like a mouse or touchscreen.

• Developed with: Python and OpenCV

• Key Techniques: HSV colour detection, contour detection, morphological operations (erosion and dilation)

II. Literature Survey

The system is based on advancements in gesture recognition, colour-based object tracking, and real-time image processing. Supporting concepts include:

• HSV Colour Space: Enables effective tracking in various lighting conditions

• Morphological Operations: Used to refine detection by removing noise

• Contour Analysis: Identifies and tracks the marker's central point for drawing

Similar Applications:

• Gesture-based interfaces in AR/VR

• Virtual whiteboards in smart classrooms

• Educational platforms for computer vision

III. Proposed System

• Workflow:

  1. Capture webcam frames

  2. Convert to HSV colour space

  3. Create a mask to isolate the coloured marker

  4. Apply erosion and dilation to refine the mask

  5. Detect contours and track the center

  6. Store coordinates and draw strokes on a canvas

• Key Components:

  • Canvas for drawing

  • Trackbars for tuning HSV range

  • Real-time rendering of strokes on both video feed and digital canvas

IV. Comparison with Traditional Systems

Traditional systems (e.g., stylus, touchscreen) require physical contact and specialized hardware. In contrast, the Air Canvas Project:

• Is touchless and gesture-based

• Requires only a webcam

• Uses computer vision instead of physical tools

V. Modules & Functionalities

1. User Interaction Module:

   • Captures finger gestures

   • Tracks marker position using HSV detection

2. Masking & Contour Detection Module:

   • Creates binary mask

   • Applies erosion/dilation

   • Finds contour center

3. Canvas & Drawing Module:

   • Stores coordinates and draws dynamic strokes on the canvas

4. Interaction Controls Module:

   • Trackbars for parameter tuning

   • Ink selection and clear canvas options

5. Optimization & Performance Module:

   • Real-time responsiveness

   • Filters out unintentional gestures

6. System Requirements:

   • Python 3, OpenCV, NumPy

   • Webcam and any OS (Windows/macOS/Linux)

7. Safety & Integrity Module:

   • Error handling for detection failures

   • System reset without restart

Conclusion

The Private Car Rental Web Portal successfully showcases how digital transformation can revolutionize the traditional car rental industry by offering a secure, peer-to-peer platform. This solution provides significant benefits to both vehicle owners and renters, such as streamlined booking processes, a robust verification system, and flexible rental options. The system’s user-friendly interface and automation reduce administrative tasks and ensure transparent transactions for all users. A standout feature of the platform is its contribution to sustainable transportation. By optimizing vehicle utilization, it helps reduce the environmental impact associated with private car ownership, promoting the sharing economy. The project has successfully met its key objectives, proving the technical feasibility of web-based rental platforms. Furthermore, it highlights the potential to reshape urban mobility. Future improvements, such as AI-powered dynamic pricing, real-time GPS tracking, and enhanced mobile functionalities, promise to further elevate the user experience. The continuous refinement of the system, driven by user feedback and technological integration, will ensure its relevance and competitiveness in the ever-evolving shared economy landscape. This platform sets a benchmark for future developments in the car rental industry.

References

[1] Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools.A foundational paper on the OpenCV library, used for real-time computer vision. [2] Rosebrock, A. (2021). Real-Time Object Tracking with OpenCV and Python. PyImageSearch.A detailed tutorial on colour tracking and object detection using OpenCV. [3] Zhang, Y., & Liu, H. (2020). Vision-Based Gesture Recognition Using Machine Learning Techniques: A Review. Sensors, 20(14), 3910.A review of gesture recognition methods with applications in HCI. [4] Sharma, A., & Bhardwaj, A. (2021). Virtual Drawing using Finger Detection and Tracking. International Journal of Computer Applications, 183(43).Implementation of a similar finger-drawing system using computer vision.

Copyright

Copyright © 2025 Kiran Ande, Anushka Korde, Deepak Dhale, Kartik Gujarkar, Hrutik Hiwase. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.