Camera Motion Detection for Stable Image Capture
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Solution Overview
Problem
Current camera phone technology often results in image blurring due to user instability, especially when capturing images for high-quality applications like OCR, as the need to manually activate buttons or joysticks introduces motion and makes capturing multiple images tedious.
Innovation Solution
A camera motion detection scheme that automatically captures images when the camera is determined to be stable by comparing the movement of obvious features in adjacent frames to a threshold, eliminating the need for manual activation and minimizing blurring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual button or joystick activation is used to capture images, then the user has control over image capture timing, but user-induced motion causes image blurring
Solution Approach 1:
The system automatically detects image stability and triggers capture without user intervention. The camera device monitors its own stability state through sensor data and autonomously determines when to capture, eliminating the need for manual button pressing that causes motion blur.
Solution Approach 2:
The system continuously monitors stability sensor data and uses this feedback to control the capture timing. When the feedback indicates stable conditions (motion below threshold), the system automatically captures the image, creating a closed-loop control system that optimizes image quality.
2Measurement precision
If the user holds the camera steady for high-quality capture, then image quality improves, but capturing multiple images becomes tedious and time-consuming
Solution Approach 1:
The camera system autonomously monitors stability and automatically triggers capture when conditions are optimal, eliminating the need for users to manually time multiple capture attempts. This self-service approach maintains high image quality while dramatically improving capture efficiency.
Solution Approach 2:
The system performs preliminary stability assessment continuously before capture is needed. By having stability monitoring already in place and ready, the system can immediately capture when stable conditions occur, without requiring users to prepare or wait for optimal conditions.
3Productivity
If automatic capture is implemented without stability detection, then capture efficiency improves, but image quality deteriorates due to motion blur
Solution Approach 1:
The system uses real-time stability sensor feedback to control automatic capture timing. The feedback loop continuously monitors motion levels and only triggers capture when stability thresholds are met, ensuring high image quality while maintaining automatic operation efficiency.
Solution Approach 2:
The system replaces manual mechanical button pressing with automated sensor-based control. Stability sensors and processing algorithms substitute for user action, automatically determining capture timing based on measured physical conditions rather than human intervention.
4Measurement precision
If multiple manual capture attempts are made to ensure quality, then image quality may improve, but time consumption increases
Solution Approach 1:
The system performs preliminary and continuous stability assessment automatically, so when stable conditions occur, capture happens immediately on the first attempt. This eliminates the need for multiple retry attempts and significantly reduces the time required to obtain quality images.
Solution Approach 2:
The camera system autonomously manages the capture process by monitoring stability and triggering capture at optimal moments without user intervention. This self-service approach ensures high image quality while minimizing capture time by eliminating manual trial-and-error attempts.
Data Source
AI summary
A device for utilizing camera motion detection for a camera input interface includes a feature extractor, a feature tracker and a capture module. The feature extractor is configured to determine at least one obvious feature in an image frame. The feature tracker is in communication with the feature extractor. The feature tracker is configured to determine an amount of movement of the at least one obvious feature in a subsequent image frame and compare the amount of movement to a threshold. The capture module is in communication with the feature tracker. The capture module is configured to capture an image in response to the comparison of the amount of movement to the threshold.