Hand Pose Classification for Fast Gesture Recognition
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Solution Overview
Problem
Current gesture recognition technologies face challenges in achieving fast and high-precision recognition of target gestures using a single image frame, particularly in scenarios like tabletop games, where accurate hand pose detection and positioning are crucial.
Innovation Solution
A method and apparatus for gesture recognition that involves hand detection using a deep learning network, followed by classification through a hand classification neural network, to determine if the hand pose within a bounding box conforms to a target gesture, while ensuring the hand is located in a pre-defined area, thereby enabling precise gesture recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional gesture recognition methods are used, then recognition can be performed, but the recognition speed and precision are insufficient for single image frame scenarios
Solution Approach 1:
The gesture recognition process is segmented into distinct stages: hand detection to obtain bounding boxes, hand pose classification using a dedicated neural network, and position verification against pre-defined areas. This segmentation allows each component to be optimized independently, achieving fast and accurate recognition within a single image frame.
Solution Approach 2:
The system performs preliminary actions by pre-defining valid hand positions and training the hand classification neural network in advance with labeled hand images. This preliminary preparation enables the system to make rapid decisions during actual gesture recognition without requiring complex real-time computations.
2Reliability
If hand detection and classification are performed comprehensively, then recognition accuracy improves, but computational complexity increases
Solution Approach 1:
The system separates hand detection and hand classification into independent modules. The detection module identifies hand regions and provides bounding boxes, while the classification module specifically determines hand poses using a dedicated neural network. This modular segmentation improves reliability through specialized processing while managing complexity by dividing the system into manageable components.
Solution Approach 2:
The hand classification neural network serves as an intermediary between raw hand detection results and final gesture recognition. It takes bounding boxes from the detection module, performs specialized pose classification, and outputs results for gesture determination, thereby simplifying the overall system architecture while improving reliability.
3Measurement precision
If position constraints are enforced within pre-defined areas, then gesture recognition accuracy improves, but the system becomes more restrictive
Solution Approach 1:
The system applies position constraints locally within pre-defined areas rather than uniformly across the entire image. Each pre-defined area corresponds to a specific region where certain gestures are expected to occur. This local application of constraints improves precision for expected gestures while maintaining flexibility for other regions or gesture types.
Data Source
AI summary
Disclosed are a gesture recognition method and apparatus, an electronic device, and a storage medium. The method includes: obtaining a hand detection result by detecting a to-be-processed image; in response to the hand detection result including a bounding box of hand, determining that a hand pose in the bounding box of hand conforms to a hand pose in a target gesture; determining that the hand is located in a pre-defined area of the to-be-processed image; and determining that the hand makes the target gesture.