Dynamic Gesture Recognition via Frame Segmentation and Inter-Frame Difference Analysis
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Solution Overview
Problem
Current gesture recognition systems are limited to recognizing only static images, which restricts them to simple gestures like V-signs and OK gestures, lacking the naturalness and information richness of dynamic gestures.
Innovation Solution
A method and apparatus for recognizing dynamic gestures by positioning a dynamic gesture in a video stream, capturing image blocks from multiple frames, generating a detection sequence, and performing recognition based on this sequence, utilizing both image blocks and inter-frame differences to identify varying dynamic gestures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static image-based gesture recognition is used, then the system is simple and fast, but it can only recognize simple gestures like V-signs and OK gestures, lacking naturalness and information richness
Solution Approach 1:
The video stream is segmented into multiple image frames, and each frame is further segmented to extract image blocks corresponding to the gesture region. This segmentation allows the system to process dynamic gestures frame-by-frame while maintaining computational efficiency by focusing only on relevant regions.
Solution Approach 2:
The system transitions from static image analysis to dynamic video stream analysis. By capturing gestures across multiple time frames and computing inter-frame differences, the system can recognize dynamic gestures (e.g., waving, pointing movements) that convey additional temporal information and naturalness.
2Adaptability or versatility
If dynamic gesture recognition using video streams is implemented, then naturalness and information richness are improved, but recognition speed and real-time performance deteriorate
Solution Approach 1:
The video stream is divided into discrete image frames, and each frame is segmented to extract only the relevant gesture region (image block). This reduces the processing load by focusing computation on small regions rather than entire frames, enabling faster dynamic gesture recognition.
Solution Approach 2:
Instead of processing every pixel and frame completely, the system extracts only the necessary image blocks corresponding to gesture regions and computes differences only between relevant frames. This partial processing approach maintains recognition accuracy while significantly improving real-time performance.
3Loss of information
If multiple image frames are processed to capture dynamic gestures, then gesture information richness is improved, but computational complexity and processing time increase
Solution Approach 1:
The system extracts only the essential information needed for gesture recognition by computing inter-frame differences and selecting relevant image blocks. This extraction process filters out redundant information while preserving the temporal dynamics and spatial characteristics necessary for accurate gesture recognition.
Solution Approach 2:
The system pre-processes video frames to identify gesture regions and extract image blocks before performing the main recognition computation. This preliminary action organizes the data in advance, reducing the complexity of subsequent processing and enabling efficient dynamic gesture recognition.
Data Source
AI summary
A method for recognizing a dynamic gesture includes: positioning a dynamic gesture in a video stream to be detected to obtain a dynamic gesture box; capturing an image block corresponding to the dynamic gesture box from each of multiple image frames of the video stream; generating a detection sequence based on the captured image block; and performing dynamic gesture recognition according to the detection sequence.