Optical Navigation Frame Segmentation for Power and Speed
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Solution Overview
Problem
Optical navigation systems face challenges in reducing power consumption and increasing tracking speed, particularly in wireless computer mice where efficient power management is crucial.
Innovation Solution
The system selectively uses portions of captured frame data for cross-correlation, determining if a frame includes prominent trackable features to either cross-correlate the entire frame or a portion of it with another frame, reducing the number of calculations and thus power consumption or increasing tracking speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire frame of image data is used for cross-correlation, then tracking accuracy is maintained, but power consumption increases and tracking speed decreases
Solution Approach 1:
The patent divides the frame of image data into multiple regions (e.g., first region and second region) and selectively processes only certain regions for cross-correlation. This segmentation allows the system to maintain tracking accuracy in regions with prominent features while reducing computational load in regions without useful features, thereby lowering power consumption.
Solution Approach 2:
The patent applies different processing qualities to different regions of the frame. Regions containing prominent trackable features are processed with full cross-correlation to maintain accuracy, while regions without prominent features use reduced processing. This local quality approach ensures tracking accuracy is maintained where needed while reducing overall power consumption.
2Measurement precision
If the entire frame of image data is used for cross-correlation, then tracking accuracy is maintained, but tracking speed decreases
Solution Approach 1:
The patent segments the frame into multiple regions and performs cross-correlation only on selected regions containing prominent features. This segmentation reduces the total number of pixels processed, thereby increasing tracking speed while maintaining accuracy in the processed regions.
Solution Approach 2:
The patent applies partial action by processing only a portion of the frame (regions with prominent features) rather than the entire frame. This partial processing approach achieves sufficient tracking accuracy for the actual tracking needs while significantly reducing computational time and increasing tracking speed.
3Use of energy by moving object
If portions of frame data are used for cross-correlation, then power consumption is reduced and tracking speed is increased, but tracking accuracy may be compromised
Solution Approach 1:
The patent ensures that regions containing prominent trackable features are identified and processed with full cross-correlation, maintaining high tracking accuracy in these critical areas. The feature detector identifies regions with sufficient feature content, ensuring that accuracy is preserved where it matters most while reducing processing elsewhere.
Solution Approach 2:
The patent employs a feature detection mechanism that analyzes the frame to identify regions with prominent trackable features. This feedback loop ensures that cross-correlation is applied to regions that will actually contribute to accurate tracking, thereby maintaining tracking accuracy while minimizing unnecessary processing in regions without useful features.
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 significantly reduces power consumption and enhances tracking performance, especially during high-speed usage by minimizing the number of multiplications required for cross-correlation, allowing for faster frame rates and improved navigation accuracy.
Implementation Method 1
an image sensor array to receive the light reflected from the navigation surface
Implementation Method 2
The comparisons are based on detecting and computing displacements of features in the captured frames of image data, which involve performing cross-correlations on the frames of image data
Data Source
AI summary
A system and method for performing optical navigation selectively uses portions of captured frame of image data for cross-correlation for displacement estimation, which can reduce the power consumption and/or increase the tracking performance at higher speed usage.