Shared Memory Architecture for Sign Language Gesture Recognition
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Solution Overview
Problem
Existing automated pattern and gesture recognition systems face inefficiencies in data management and workload distribution, particularly in real-time sign language translation, due to unnecessary data transfers and conversions, which hinder performance and accuracy.
Innovation Solution
The implementation of a system with multiple processing units, shared memory, and customized templates that define attribute values for data storage and access, allowing direct access to shared memory without copying data, and dynamic resource adjustment for improved computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is copied to separate memory locations for each processing unit, then each processing unit can independently access its data, but data transfer latency increases and memory usage increases
Solution Approach 1:
Multiple processing units share a common memory location instead of each having separate copies. The patent implements a shared memory architecture where gesture data is stored once and accessed by multiple processing units simultaneously, eliminating redundant data transfers and reducing latency while maintaining independent access capability.
2Ease of operation
If data is copied to separate memory locations for each processing unit, then data access is simplified, but memory usage increases
Solution Approach 1:
The patent consolidates gesture data storage into a single shared memory location that is accessible by multiple processing units. This eliminates the need for redundant data copies in each processing unit's memory, significantly reducing overall memory consumption while maintaining efficient access through the shared memory interface.
3Productivity
If customized templates with attribute values are used to define data storage locations, then data management efficiency is improved, but system complexity increases
Solution Approach 1:
The patent pre-defines templates with attribute values that specify storage locations and data characteristics before processing begins. These templates are configured in advance to match expected data patterns, allowing processing units to efficiently locate and access gesture data without runtime complexity. The templates serve as pre-computed lookup structures that simplify real-time data management.
Data Source
AI summary
Disclosed are methods, apparatus and systems for improving data management and workload distribution in pattern recognition systems. An example method of managing data for a sign language translation system includes receiving multiple sets of data acquired by one or more data acquisition devices. Each set of data including an image frame that illustrates at least a part of a gesture. The method includes determining, for each of the multiple sets of data, a plurality of attribute values defined by a customized template. The method includes accessing the multiple sets of data, by a plurality of processing units, based on a location indicated by the attributes for recognizing the at least a part of a gesture.