Camera Movement Compensation for Gesture Detection
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Solution Overview
Problem
Wearable devices with camera-equipped systems face challenges in accurately detecting gestures due to camera movement, leading to erroneous recognition and interpretation of gestures, as existing methods require users to remain still and often misinterpret stationary object motion as gestures.
Innovation Solution
The system uses a camera in conjunction with a motion sensing device to analyze motion data from sequential frames, rejecting or adjusting frames where camera movement exceeds a predetermined threshold, and replacing rejected frames with duplicates of the most recent accepted frame to provide a consistent, motion-free video stream for improved gesture detection and object recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional gesture detection methods are used in wearable devices, then the system can detect gestures, but the accuracy deteriorates due to camera movement causing stationary objects to appear moving
Solution Approach 1:
The patent introduces an intermediary processing step between raw video capture and gesture detection. A motion estimation algorithm analyzes sequential video frames to calculate camera movement vectors, which then serve as correction data for stabilizing the video stream before gesture recognition occurs. This intermediary motion compensation mechanism separates the effects of camera movement from actual gesture movements.
Solution Approach 2:
The system performs preliminary motion estimation and compensation before gesture detection takes place. By pre-calculating camera movement from sequential frames and applying stabilization transformations, the system proactively counteracts the harmful effect of camera motion before it can interfere with gesture recognition, ensuring more accurate detection.
2Measurement precision
If the user remains still to improve gesture detection accuracy, then detection accuracy improves, but the ease of operation deteriorates as users cannot move naturally
Solution Approach 1:
The patent replaces the mechanical requirement for user stillness with an algorithmic motion compensation system. Instead of requiring the physical camera to remain stationary or the user to maintain a fixed position, the system uses computational methods to stabilize the video stream, substituting mechanical constraints with software-based motion correction.
3Measurement precision
If motion compensation is applied to all frames, then gesture detection accuracy improves, but the processing time and computational load increase
Solution Approach 1:
The system applies motion compensation selectively rather than uniformly to all frames. Motion estimation is performed on key reference frames, and compensation is applied only where necessary based on detected movement thresholds. This partial application of motion correction reduces overall processing load while maintaining sufficient accuracy for gesture detection.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of gesture identification and recognition by minimizing errors caused by camera movement, allowing for easier and more accurate detection without the need for users to remain still, and provides a video stream suitable for applications requiring consistent motion-free video.
Implementation Method 1
analyze motion data associated with the sequential frames of a video stream
Data Source
AI summary
A method, system, apparatus, and/or device for adjusting or removing frames in a set of frames. The method, system, apparatus, and/or device may include: associating a first frame of a set of frames with motion data that is captured approximately contemporaneously with the first frame; when a sampling rate of the motion data is greater than a frame rate of the set of frames, aggregating a first sample of the motion data captured at the first frame and a second sample of the motion data captured between the first frame and a second frame of the set of frames to obtain a movement value; when the movement value does not exceed a first threshold value, accepting the first frame from the set of frames; and when the movement value exceeds the first threshold value, rejecting the first frame from the set of frames.