Hand Keypoint Detection With Heat Maps for Mobile Gesture Recognition
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Solution Overview
Problem
Existing methods for detecting hand key points in mobile terminals suffer from long calculation times and poor real-time performance, limiting the application of gesture recognition in mobile devices.
Innovation Solution
A method involving a pre-trained heat map model to predict two-dimensional coordinates of hand key points, followed by a three-dimensional information prediction model to determine three-dimensional coordinates using hand structured connection information, reducing the complexity and calculation load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a method for detecting hand key points is applied to mobile terminals, then gesture recognition can be used in mobile devices, but the calculation time is long and real-time performance is poor
Solution Approach 1:
The patent divides the hand key point detection task into multiple processing stages: image acquisition, heat map generation, 3D coordinate prediction, and post-processing. By segmenting the detection process into discrete steps with specialized models for each stage, the system optimizes calculation efficiency at each step while maintaining overall detection accuracy, thereby reducing total calculation time for mobile terminal applications
2Adaptability or versatility
If a method for detecting hand key points is applied to mobile terminals, then gesture recognition can be used in mobile devices, but real-time performance is poor
Solution Approach 1:
The patent replaces traditional complex mechanical computation methods with neural network-based heat map models and 3D prediction models. These learned models perform predictions through efficient matrix operations rather than iterative mechanical calculations, significantly improving real-time performance on mobile terminals while maintaining detection accuracy
Data Source
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AI summary
A hand key point detection method, a gesture recognition method, and related devices. The hand key point detection method comprises: obtaining a hand image under detection (S101); inputting the hand image into a pre-trained thermodynamic diagram model to obtain a thermodynamic diagram of hand key points, the thermodynamic diagram comprising two-dimensional coordinates of the hand key points (SI02); inputting the thermodynamic diagram and the hand image into a pre-trained three-dimensional information prediction model to obtain hand structured connection information (S103); and determining three-dimensional coordinates of the hand key points in the world coordinate system according to the hand structured connection information and the two-dimensional coordinates in the thermodynamic diagram (S104).