Head-Mounted Gesture Recognition Using Region Motion Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing gesture recognition technologies in electronic devices like smart glasses struggle with accuracy in various use situations, particularly in low-light conditions or when users wear gloves, leading to unstable gesture detection.
Innovation Solution
A gesture recognition apparatus and method that includes a portion detection unit to identify hand positions and joints, an important-region detection unit to pinpoint relevant motion areas, and a motion detection unit to track these regions, enhancing gesture recognition accuracy through a combination of image processing and potentially deep learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gesture recognition is performed using traditional image processing methods (hand extraction and shape specification), then the system can operate with simpler processing, but the recognition accuracy deteriorates in challenging conditions such as low light or when users wear gloves
Solution Approach 1:
The patent segments the gesture recognition process into distinct functional units: portion detection unit (detecting hand portions from captured images), region detection unit (detecting important regions within the hand portions), and motion detection unit (detecting motion of the important regions). This segmentation allows each unit to specialize in a specific aspect of gesture recognition, improving overall accuracy while maintaining manageable processing complexity through modular architecture.
Solution Approach 2:
The patent transitions from traditional 2D shape-based recognition to 3D motion-based recognition by detecting the motion of important regions over time. The motion detection unit analyzes temporal changes in the important regions detected by the region detection unit, adding a temporal dimension to the recognition process. This dimensional enhancement enables accurate gesture recognition even when hand shapes are obscured by gloves or poor lighting conditions.
2Reliability
If the system focuses on detecting motion of specific regions rather than overall hand shape, then gesture recognition accuracy improves in various conditions, but the processing complexity increases
Solution Approach 1:
The patent applies local quality by having the region detection unit identify and focus on specific important regions within the hand portions detected by the portion detection unit. Instead of analyzing the entire hand or uniform regions, the system concentrates computational resources on detecting motion of key regions that are most informative for gesture recognition. This localized approach improves reliability by focusing on discriminative features while managing complexity through selective processing.
Solution Approach 2:
The patent implements preliminary action by having the portion detection unit and region detection unit identify and prepare the important regions before the motion detection unit performs the actual gesture recognition. The system pre-processes the captured images to detect hand portions and then further detects important regions within those portions, preparing the data in advance for the motion detection stage. This preliminary processing improves reliability by ensuring that the motion detection unit receives pre-processed, relevant data.
Data Source
AI summary
A gesture recognition apparatus according to the present invention includes at least one memory and at least one processor which function as: a portion detection unit configured to detect from a captured image a portion making a gesture; a recognition unit configured to recognize the gesture on a basis of motion of the portion detected by the portion detection unit; a region detection unit configured to detect from the portion a region to be used for the gesture; and a motion detection unit configured to detect motion of the region detected by the region detection unit, wherein the recognition unit recognizes the gesture on a basis of the motion detected by the motion detection unit.