Structured Light Gesture Recognition for AR Input
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Solution Overview
Problem
Existing user interfaces fail to effectively detect and interpret hand gestures within an environment for providing input to automated systems, limiting the ability to use physical movements as commands in augmented reality and similar applications.
Innovation Solution
The system employs a computing device with a projector and camera to capture and analyze hand gestures using structured light, estimating motion parameters and comparing them to a library of reference gestures to classify and interpret user inputs, allowing for dynamic motion modeling and gesture recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional input devices like keyboards are used, then physical manipulation is required, but user motion detection and gesture recognition are not enabled
Solution Approach 1:
The patent replaces mechanical input devices (keyboards, mice) with an optical sensing system using structured light projection and camera capture. The system projects structured light patterns onto the environment, captures images with a camera, and processes the light pattern distortions to detect hand gestures and motions, eliminating the need for physical mechanical input devices.
Solution Approach 2:
The patent introduces structured light patterns as an intermediary medium between the user and the computing system. The structured light serves as a mediator that interacts with the user's hand gestures, allowing the system to detect and interpret motions without direct mechanical contact or complex sensor arrays.
2Measurement precision
If structured light and camera systems are implemented, then gesture detection capability is improved, but device complexity increases
Solution Approach 1:
The patent segments the gesture recognition task into distinct processing stages: structured light projection, image capture, light pattern analysis, motion detection, and gesture classification. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by breaking down the complex problem into manageable sub-tasks.
Solution Approach 2:
The patent adds the dimension of structured light patterns to the imaging system, transforming standard 2D image capture into a 3D-aware detection system. By projecting and analyzing light pattern distortions across multiple dimensions, the system achieves precise gesture detection without requiring complex multi-camera arrays or advanced sensor technology.
3Measurement precision
If motion parameters are estimated and compared to reference gestures, then gesture classification accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing the structured light patterns and establishing reference gesture libraries before actual gesture recognition occurs. Motion parameters are pre-calculated and stored as reference data, allowing the system to quickly compare captured gestures against pre-established patterns rather than performing full analysis in real-time.
Solution Approach 2:
The patent implements partial action by focusing computational resources on key motion parameters and critical gesture features rather than analyzing every detail of the captured motion. The system identifies and processes only the most relevant parameters for gesture classification, reducing overall processing time while maintaining high accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate classification and interpretation of hand gestures, allowing users to interact with augmented reality systems through natural movements, enhancing user input and command recognition in various environments.
Implementation Method 1
one or more projectors configured to project the structured light pattern into the scene
Implementation Method 2
one or more cameras configured to capture images representative of the scene and the reflected light pattern
Data Source
AI summary
A hand gesture may be characterized mathematically as a set of motion parameters applied to a dynamic motion model. Training may be conducted to compile a library of motion parameter sets corresponding to various gestures. Motion parameters corresponding to observed gestures may than be compared to the library of motion parameter sets to classify the observed gestures.