Optical Flow Tilt Sensor Sparse Feature Motion Estimation
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Solution Overview
Problem
Existing optical flow systems for motion estimation are computationally expensive and unsuitable for consumer handheld devices, which require efficient and real-time processing for determining camera motion from captured images.
Innovation Solution
Implementing a method that uses a sparse feature set and sparse flow field to estimate camera motion in two or three degrees of freedom, approximating motion by ignoring negligible degrees of freedom, and employing optical flow analysis to determine user input for applications like games and security systems on devices with limited processing power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional optical flow systems are used for motion estimation, then measurement precision is improved, but device complexity and computational cost increase
Solution Approach 1:
The patent segments the optical flow computation by dividing the image into regions and processing only significant features rather than all pixels. This segmentation approach maintains measurement precision for motion estimation while reducing computational complexity by focusing resources on key motion-carrying regions.
Solution Approach 2:
The patent applies local quality by using adaptive processing where computation intensity varies across different image regions. Areas with significant motion or features receive higher processing quality, while uniform or static regions receive reduced processing, thereby maintaining overall measurement precision while reducing total computational load.
2Measurement precision
If full optical flow analysis is performed, then measurement precision is improved, but productivity decreases due to processing time
Solution Approach 1:
The patent implements partial action by performing optical flow analysis only on selected features and regions rather than complete image processing. This partial processing approach maintains sufficient measurement precision for practical applications while achieving real-time productivity by reducing the total computational workload.
Solution Approach 2:
The patent uses periodic action through frame-based processing where optical flow is computed at discrete time intervals rather than continuously. This periodic approach maintains measurement precision for motion detection while improving productivity by allowing processing to occur in manageable periods suitable for real-time systems.
3Measurement precision
If dense feature tracking is used, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent extracts and tracks only the most significant features from the image rather than performing dense feature tracking. This extraction approach maintains measurement precision by focusing on key motion-carrying features while reducing energy consumption by eliminating computation for less significant regions.
Data Source
AI summary
A method is described for determining a description of motion of a moving mobile camera to determine a user input to an application. The method may involve capturing a series of images from a moving mobile camera and comparing stationary features present in the series of images. Optical flow analysis may be performed on the series of images to determine a description of motion of the moving mobile camera. Based on the determined motion, a user input to an application may be determined and the application may respond to the user input, for example, by updating a user interface of the application.