Single Camera Hand Tracking Using SIFT and Pose Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional hand tracking systems require complex setups with colored gloves, retro-reflective markers, or multiple cameras, limiting their ability to perform real-time hand tracking and pose classification with high accuracy and flexibility.
Innovation Solution
A system utilizing a single camera with scale invariant feature transforms (SIFT) and pixel intensity/displacement descriptors for real-time hand tracking and pose classification, enabling the use of bare hands and allowing for intuitive control of consumer electronics through gestures, without the need for additional markers or gloves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional hand tracking systems use colored gloves, retro-reflective markers, or multiple cameras, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts and removes the need for external markers, gloves, or multiple camera systems by using only a single camera to capture hand images. The system processes natural hand appearance features directly from standard video feeds, eliminating complex auxiliary equipment while maintaining tracking capability through image processing algorithms that analyze hand geometry and motion patterns
Solution Approach 2:
The single camera system performs multiple functions: it captures hand tracking data, extracts pose information, and provides gesture recognition all through one device. The system universally handles various hand positions and gestures without requiring specialized equipment for each function, replacing multiple dedicated devices with a single multi-functional camera system
2Reliability
If conventional hand tracking systems use multiple cameras or markers, then reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The system uses the user's natural hand appearance and motion without requiring them to wear special equipment like gloves or markers. The hand itself serves as the tracking target, eliminating the need for users to attach or wear additional devices, thereby improving ease of operation while maintaining reliable tracking through algorithmic analysis of natural hand features
3Device complexity
If a single camera is used for hand tracking, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent compensates for the limitations of a single 2D camera by analyzing temporal dimensions (video sequences over time) and extracting multiple feature dimensions from the images. The system processes hand pose information across multiple frames and uses machine learning algorithms to infer 3D hand configurations from 2D image data, adding dimensional analysis to overcome the single-camera constraint and maintain measurement precision
Data Source
AI summary
A hand gesture from a camera input is detected using an image processing module of a consumer electronics device. The detected hand gesture is identified from a vocabulary of hand gestures. The electronics device is controlled in response to the identified hand gesture. This abstract is not to be considered limiting, since other embodiments may deviate from the features described in this abstract.