Video Sign Alias Assignment System
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Solution Overview
Problem
Existing technologies face challenges in efficiently recognizing and processing sign languages in video data, particularly in handling deictic elements and assigning aliases to signs and objects.
Innovation Solution
The system employs a combination of hand keypoint extraction, sign language classification models, object detection, and gesture recognition to identify signs, objects, and deictic elements in video data, enabling the assignment of aliases and simplifying the use of anaphoric elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sign language recognition methods are used, then the system can process basic signs, but it cannot accurately recognize deictic elements and assign aliases to signs and objects
Solution Approach 1:
The system segments the sign language processing into distinct components: hand keypoint extraction, sign classification, object detection, and deictic element identification. Each component handles specific aspects independently, allowing accurate recognition of deictic elements while maintaining overall system accuracy.
Solution Approach 2:
The system introduces an intermediary alias assignment mechanism that bridges signs and objects. Deictic signs are identified and assigned aliases that reference specific objects in the scene, enabling the system to handle referential expressions accurately without compromising recognition precision.
2Measurement precision
If the system processes all signs in detail, then recognition accuracy is maintained, but processing efficiency decreases
Solution Approach 1:
The system performs preliminary hand keypoint extraction and sign classification before full processing. By identifying deictic elements early in the processing pipeline and assigning aliases, the system avoids redundant detailed processing while maintaining accuracy, thus improving efficiency.
Solution Approach 2:
The system creates alias copies of deictic signs that reference objects. Instead of processing each deictic reference in full detail, the system uses lightweight alias representations that maintain recognition accuracy while reducing processing overhead for subsequent operations.
3Adaptability or versatility
If the system uses complex processing methods, then it can handle deictic elements, but device complexity increases
Solution Approach 1:
The system implements a universal alias assignment mechanism that handles multiple functions: identifying deictic signs, linking them to objects, and managing anaphoric references. This multi-functional approach enables deictic element handling without requiring separate complex subsystems for each function.
Solution Approach 2:
The system uses the existing hand keypoint extraction and object detection infrastructure to support deictic element handling. Rather than adding entirely new complex components, the system leverages existing modules and adds an alias assignment layer that integrates with the current architecture, minimizing overall complexity.
Data Source
AI summary
A system receives an image that includes a person executing a sign. The system determines whether the sign is a sign of a sign language, the sign points to an object in the image, the sign indicates that the person wants to assign an alias to the sign of the sign language or the object in the image, and/or whether the sign uses the alias. After this determination, the system executes an action based on whether the sign comprises the sign of the sign language, the sign identifying the object in the image, the sign assigning the alias, or the sign using the alias.