3D CNN Gesture Recognition via Motion-Pose Fusion
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Solution Overview
Problem
Current sign language translation technologies face challenges in accurately capturing and processing the complexity of hand movements and non-manual activities across different sign languages, leading to inadequate translation quality and limited adaptability to various sign languages.
Innovation Solution
The use of a neural network system combining 3D convolutional neural networks (3D CNNs) and recurrent neural networks (RNNs) with long short-term memory (LSTM) units, which processes images to identify gestures by fusing motion, pose, and color information, and determines whether the recognition corresponds to a singular gesture across multiple images, enabling flexible translation between various sign languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used for sign language translation, then the system complexity is low, but the translation accuracy and ability to capture gesture complexity is insufficient
Solution Approach 1:
The system segments the complex task of sign language recognition into multiple specialized neural network components: 3D CNNs for extracting spatial and temporal features from video sequences, RNNs for modeling temporal dependencies in gesture sequences, and LSTM units for capturing long-term dependencies. Each component handles a specific aspect of gesture analysis, improving overall accuracy while managing complexity through functional decomposition.
Solution Approach 2:
The patent transitions from traditional 2D image processing to 3D convolutional neural networks that process video data in three dimensions (height, width, and time). This dimensional extension enables the system to capture temporal dynamics and motion patterns essential for sign language recognition, significantly improving gesture recognition accuracy by analyzing spatio-temporal features rather than static frames.
2Measurement precision
If multiple neural network components are used to process different aspects of gestures, then the recognition accuracy improves, but the processing time and computational load increase
Solution Approach 1:
The system merges multiple neural network components (3D CNNs, RNNs, and LSTMs) into an integrated architecture where feature extraction and temporal modeling operations are combined in a unified processing pipeline. This integration allows parallel processing of spatial and temporal features, reducing overall processing time while maintaining high recognition accuracy through coordinated multi-component analysis.
Solution Approach 2:
The 3D CNN performs preliminary feature extraction from raw video data, converting complex visual information into condensed spatio-temporal feature representations before passing them to the RNN and LSTM components. This preliminary processing reduces the computational burden on subsequent stages, enabling faster processing while preserving essential gesture characteristics for accurate identification.
3Ease of operation
If the system processes detailed motion and pose information, then the translation naturalness improves, but the computational resources required increase
Solution Approach 1:
The system extracts only the most relevant spatio-temporal features from comprehensive video data using 3D CNNs, selectively identifying motion patterns, pose transitions, and gesture dynamics that are critical for natural sign language translation. This selective extraction maintains translation naturalness by preserving essential gesture characteristics while discarding redundant information, thereby reducing computational energy consumption.
4Adaptability or versatility
If the neural network is trained on large amounts of sign language data, then the adaptability to different dialects improves, but the training time and data processing requirements increase
Solution Approach 1:
The neural network architecture is designed with universal components that can process multiple sign language dialects through a single unified model. The 3D CNN and RNN layers are configured to learn dialect-specific patterns while maintaining generalizable feature extraction capabilities, allowing the system to adapt to different sign language varieties without requiring separate specialized models, thus improving versatility while managing training efficiency.
Data Source
AI summary
Disclosed are methods, apparatus and systems for gesture recognition based on neural network processing. One exemplary method for identifying a gesture communicated by a subject includes receiving a plurality of images associated with the gesture, providing the plurality of images to a first 3-dimensional convolutional neural network (3D CNN) and a second 3D CNN, where the first 3D CNN is operable to produce motion information, where the second 3D CNN is operable to produce pose and color information, and where the first 3D CNN is operable to implement an optical flow algorithm to detect the gesture, fusing the motion information and the pose and color information to produce an identification of the gesture, and determining whether the identification corresponds to a singular gesture across the plurality of images using a recurrent neural network that comprises one or more long short-term memory units.