Two-Stream Sign Recognition Using Key Postures and AVM Motion
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Solution Overview
Problem
Conventional sign language recognition systems fail to effectively capture spatiotemporal information using a small number of frames, particularly for dynamic gestures, leading to degraded recognition accuracy due to variations between signers.
Innovation Solution
A sign language recognition system utilizing a motion sensor, key postures extractor, accumulative video motion (AVM) module, and a two-stream network (DMN and AMN) to extract and fuse spatiotemporal information, transforming motion into a single AVM image for efficient classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional sign language recognition systems use a small number of frames for recognition, then processing speed is improved, but recognition accuracy deteriorates due to inability to capture spatiotemporal information
Solution Approach 1:
The system segments the video stream into two independent processing streams: a spatial stream that extracts static posture features from individual frames, and a temporal stream that captures motion dynamics through frame differencing and accumulation. This segmentation allows each stream to process with minimal frames while maintaining comprehensive spatiotemporal understanding
Solution Approach 2:
The system transforms temporal information into a spatial representation by accumulating motion differences across frames to create a motion map. This converts the temporal dimension of motion sequences into a spatial pattern that can be analyzed alongside static postures, enabling accurate recognition with fewer frames
2Measurement precision
If conventional systems consider all frames for sign learning and classification, then recognition accuracy is improved, but processing time increases and accuracy degrades due to variations between signers
Solution Approach 1:
The system extracts only the essential spatiotemporal features from the video stream rather than processing all frames. The spatial stream extracts key posture information from selected frames, while the temporal stream extracts motion dynamics through differencing, discarding redundant temporal information that does not contribute to recognition
Solution Approach 2:
The system dynamically adapts the number of frames processed based on the signer's movement patterns. The temporal stream accumulates motion differences until sufficient motion information is captured, allowing the system to process fewer frames for slow gestures while automatically capturing more frames for rapid movements, optimizing processing time while maintaining accuracy
3Device complexity
If conventional systems fail to employ temporal learning techniques, then device complexity is reduced, but the system cannot learn non-manual gestures efficiently
Solution Approach 1:
The system segments temporal processing into a dedicated temporal stream that handles motion analysis separately from the spatial stream. This temporal stream uses frame differencing and accumulation techniques specifically designed for capturing non-manual gestures, ensuring reliable recognition without significantly increasing overall system complexity
Solution Approach 2:
The system introduces motion difference frames as an intermediary representation between raw video frames and final classification. These intermediate motion maps capture temporal dynamics in a compressed form that can be processed efficiently by the neural network, enabling temporal learning without proportionally increasing computational complexity
Data Source
AI summary
A sign language recognition system is described. The system includes a motion sensor, a processing circuitry and a display device. The motion sensor captures and records a dynamic sign language gesture as a sign video stream. The processing circuitry is configured with a key postures extractor, an accumulative video motion (AVM), and a sign recognition network (SRN). The key postures extractor captures main postures of the dynamic sign language gesture by extracting key frames in the sign video stream. The AVM captures motion of the sign video stream frames and transforms the motion in an AVM frame into a single AVM image. The SRN is configured as a convolutional network. The main postures and AVM image are fed into a two-stream network. The features from the two stream network are concatenated and fed into the SRN for learning fused features and performing classification of the sign language gesture.