Gesture Recognition Using Bidirectional Recurrent Classification
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Solution Overview
Problem
Current gesture recognition systems face challenges in accurately identifying dynamic hand and body gestures, particularly in controlling vehicle functions like IVIS, as they often rely on instantaneous image frames or lack effective temporal information processing.
Innovation Solution
The method employs difference images, obtained by subtracting previous frames from current frames or using event cameras, and incorporates bidirectional recurrent classification heads with time-gated self-attention layers to classify dynamic gestures, providing robust movement-based gesture recognition suitable for vehicle control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If instantaneous image frames are used for gesture recognition, then the system is simpler and faster, but the accuracy of identifying dynamic gestures deteriorates
Solution Approach 1:
The system performs preliminary action by capturing multiple sequential image frames before the gesture is completed. These pre-captured frames are then processed to extract movement information, allowing the system to accurately identify dynamic gestures without requiring complex real-time processing during the gesture execution.
Solution Approach 2:
The invention introduces dynamics by transitioning from static instantaneous frames to dynamic sequential frames. The system processes a sequence of images to capture movement patterns, enabling accurate recognition of dynamic gestures while maintaining computational efficiency through optimized processing of the temporal sequence.
2Measurement precision
If difference images are used to capture movement, then gesture recognition accuracy improves, but computational complexity increases
Solution Approach 1:
The system segments the image processing task into distinct stages: capturing sequential frames, computing difference images to isolate movement, extracting features from the difference images, and classifying gestures. This segmentation allows each stage to be optimized independently, reducing overall computational complexity while maintaining accuracy.
Solution Approach 2:
The invention extracts only the necessary movement information from the sequence of images by computing difference images. This extraction approach filters out redundant information and focuses computational resources on the essential movement patterns, reducing complexity while improving accuracy.
3Measurement precision
If temporal information is added to gesture recognition, then classification accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-processing the sequence of images to extract movement features before final classification. This preliminary extraction of temporal information allows the classification process to be faster, as it only needs to process the extracted features rather than the entire video sequence in detail.
Solution Approach 2:
The invention applies partial action by processing only the necessary portion of the temporal sequence - specifically capturing enough frames to identify the gesture pattern without processing the entire video stream. This selective processing reduces time loss while maintaining accurate classification.
Data Source
AI summary
A method for identifying a gesture from one of a plurality of dynamic gestures, each dynamic gesture comprising a distinct movement made by a user over a period of time within a field of view of an image acquisition device comprises iteratively: acquiring a current image from said image acquisition device at a given time; and passing at least a portion of the current image through a bidirectionally recurrent multi-layer classifier. A final layer of the multi-layer classifier comprises an output indicating a probability that a gesture from the plurality of dynamic gestures is being made by a user during the time of acquiring the image.