Project Title: Hand Gesture Recognition with Real-Time Video Processing
Overview:
This project focuses on building a real-time hand gesture recognition system using computer vision and machine learning techniques. The system captures video feed from a webcam, processes each frame to detect hand gestures, and converts recognized gestures into corresponding textual commands or outputs.
Technologies Used:
OpenCV: For capturing and processing video frames, including operations such as flipping, drawing bounding boxes, and displaying results.
MediaPipe: Utilized for hand landmark detection to identify hand movements and extract relevant coordinates for gesture classification.
Custom Machine Learning Model: A classification model that predicts specific hand gestures based on the captured image data.
Streamlit: Provides a user-friendly interface where users can interact with the hand gesture recognition model, adjust model settings (such as the confirmation rate), and view results in real time.
Python: The core programming language driving the project, along with helper libraries for numerical operations and machine learning pipelines.
Key Features:
Real-Time Video Capture: Captures video frames from a live webcam feed, enabling continuous hand gesture detection.
Hand Detection and Landmark Processing: Detects hands in each frame and marks key points such as fingers, palm, and wrist, allowing accurate gesture tracking.
Dynamic Gesture Recognition: Predicts gestures based on the hand landmarks and converts them into corresponding text, which is displayed on the screen.
Interactive Streamlit Dashboard: A web-based interface that lets users adjust parameters (e.g., gesture confirmation rate) and observe the live prediction results.
Text Generation: Converts recognized gestures into text, allowing the system to build sentences or commands based on continuous gesture inputs.
Visual Feedback: Displays bounding boxes around detected hands and updates the text area in real time with the recognized gesture's output.
Challenges Solved:
Managing real-time frame processing and model prediction without significant lag.
Building a flexible system where gestures can be tuned and confirmed based on user-defined criteria (e.g., confirmation rates).
Ensuring high accuracy in gesture prediction by implementing a robust hand detection pipeline.
Applications:
Human-Computer Interaction (HCI): This project can be extended for controlling systems using hand gestures, improving accessibility for users with limited mobility.
Sign Language Translation: The gesture recognition system can be adapted to translate sign language into text, providing communication assistance for the hearing-impaired.
Gesture-Based Control: It can be used to build applications that allow users to control devices or interfaces using simple hand movements, like flipping between slides or adjusting settings.
