Spin State Estimation Using Dynamic Time Interval Selection
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Solution Overview
Problem
Existing techniques for estimating the spin state of an object, such as a flying ball, face accuracy issues due to fixed short sections in video analysis, leading to high error ratios and reduced estimation accuracy, especially at higher frame rates.
Innovation Solution
The technique estimates the spin state by using a target estimation image generated by spinning an object image based on various hypotheses of spin states and selecting the hypothesis with the highest likelihood, allowing for multiple w values (absolute values of 2 or more) to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tc is fixed to a small value such as 1 for spin state estimation, then the processing is simple and quick, but the error ratio becomes large and estimation accuracy deteriorates
Solution Approach 1:
The patent makes the time interval parameter dynamic by allowing it to take multiple different values (including 1 and values greater than 1) rather than being fixed. The system selectively applies different time interval values based on processing needs, transforming the static parameter into a dynamic one that can adapt between speed and accuracy requirements.
Solution Approach 2:
The patent changes the parameter of time interval (tc) from a fixed small value to multiple possible values (1, 2, 3, ...). By varying this parameter, the system can adjust the balance between processing speed and estimation accuracy, selecting appropriate values different from the conventional fixed value of 1.
2Device complexity
If a fixed short section is used for spin state estimation, then the processing complexity is low, but the estimation accuracy deteriorates due to high error ratio
Solution Approach 1:
The patent applies partial action by using multiple time interval values selectively rather than always using the maximum possible interval. This allows the system to achieve improved accuracy when needed while avoiding unnecessary complexity in cases where simpler processing suffices.
Solution Approach 2:
The system dynamically adjusts the processing complexity by varying the time interval parameter. When higher accuracy is required, it uses larger time intervals involving more frames; when speed is prioritized, it uses smaller intervals, making the complexity adaptive rather than fixed.
3Measurement precision
If multiple w values are considered for spin state estimation, then the estimation accuracy improves, but the computational load increases
Solution Approach 1:
The patent makes the computational workload dynamic by varying the number of w values processed based on requirements. The system can adjust between processing a single w value for lower computational load and multiple w values for higher accuracy, transforming the static computational effort into a dynamic parameter.
Solution Approach 2:
The patent changes the parameter of the number of hypotheses (w values) from fixed to variable. By adjusting this parameter, the system can control the trade-off between computational energy consumption and estimation accuracy, selecting appropriate numbers of w values based on specific application needs.
Data Source
AI summary
A target estimation image which is an image of a target at a time point t+w·u obtained by spinning the object in an object image at a time point t obtained from an input video of a plurality of frames in time-series by w unit time on the basis of a hypothesis of spin state and an object image at the time point t+w·u obtained from the input video are used, to estimate the spin state of the object by selecting a hypothesis of spin state and w in which likelihood of the target estimation image becomes high from among a plurality of hypotheses of spin states and a plurality of w whose absolute values are two or more, wherein an absolute value of w is an integer of 2 or more and u is a unit time.