Multi-threaded Gesture Recognition with Pose Estimation
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Solution Overview
Problem
Current sign language translation systems face challenges in achieving real-time, accurate recognition and translation due to limitations in action recognition using AI components, including suboptimal performance of neural networks and the lack of efficient methods to combine artificially intelligent components, leading to high computational requirements and time-consuming training processes.
Innovation Solution
The implementation of a multi-threaded processor system using 3D CNNs, RNNs, and optical flow for real-time gesture recognition, which captures and processes sign language data with multiple sensors and neural networks to translate between different sign languages, employing techniques like pose estimation and data compression to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional neural networks are used for sign language translation, then translation capability is provided, but computational efficiency is low and training time is excessive
Solution Approach 1:
The patent segments the sign language translation task into multiple specialized neural network components: a pose estimation network for extracting skeletal information, an optical flow network for capturing motion dynamics, and a translation network for generating target language output. This segmentation allows each component to be optimized independently and process information in parallel, dramatically reducing training time while improving overall system efficiency.
Solution Approach 2:
The patent transforms the input data from traditional 2D image frames to 3D skeletal representations with temporal dimensions. By converting visual data into pose sequences with joint coordinates and motion vectors across multiple time steps, the system creates a higher-dimensional feature space that captures essential sign language dynamics more efficiently, reducing the computational burden on traditional networks.
2Measurement precision
If multiple sensors and neural networks are used to capture and process sign language data, then recognition accuracy is improved, but hardware requirements and system complexity increase
Solution Approach 1:
The patent merges multiple data sources (RGB images, depth maps, skeletal poses, optical flow) and processing functions into a unified neural network architecture. The pose estimation and optical flow extraction are integrated as preprocessing stages within the same system, and their features are combined in the translation network. This integration reduces hardware requirements by consolidating processing functions while maintaining high recognition accuracy through multi-modal feature fusion.
3Measurement precision
If comprehensive pose estimation and optical flow processing are performed, then gesture recognition accuracy is enhanced, but computational requirements increase
Solution Approach 1:
The patent extracts only the most critical features from the input data: skeletal pose information representing key body joints and optical flow vectors capturing dominant motion patterns. By selectively extracting these essential features rather than processing complete image frames, the system achieves high gesture recognition accuracy while significantly reducing computational energy requirements. The pose estimation network outputs condensed skeletal representations that retain discriminative information for sign language recognition.
Data Source
AI summary
Disclosed are methods, apparatus and systems for real-time gesture recognition. One exemplary method for the real-time identification of a gesture communicated by a subject includes receiving, by a first thread of the one or more multi-threaded processors, a first set of image frames associated with the gesture, the first set of image frames captured during a first time interval, performing, by the first thread, pose estimation on each frame of the first set of image frames including eliminating background information from each frame to obtain one or more areas of interest, storing information representative of the one or more areas of interest in a shared memory accessible to the one or more multi-threaded processors, and performing, by a second thread of the one or more multi-threaded processors, a gesture recognition operation on a second set of image frames associated with the gesture.