Video Time-Series Motion Detection With Exposure Correction
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Solution Overview
Problem
Conventional methods for motion detection using high frame rate digital video processing fail to correct exposure errors, leading to significant measurement inaccuracies when motion duration approaches imaging exposure duration, especially when maximum exposure is used for high-quality imaging.
Innovation Solution
A method involving a processor and video sensor system that determines recording parameters, extracts data sets describing motion, calculates frequency transforms, and applies exposure correction using compensation functions to correct for measurement errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If maximum exposure duration is used for high-quality video capture, then image and video quality is improved, but motion detection accuracy deteriorates due to exposure-related measurement errors
Solution Approach 1:
The patent converts the harmful exposure-induced measurement errors into beneficial correction data. By capturing the exposure parameters (duration, timing) alongside video data, the system generates correction factors that when applied to the motion detection algorithm, actually improve measurement accuracy. The harmful distortion becomes a known variable that can be mathematically compensated for, transforming the problem into a solution.
Solution Approach 2:
The patent changes the parameter space by introducing exposure correction factors as new variables in the motion detection process. Instead of treating exposure duration as a fixed constraint, the system varies the exposure parameters and applies corresponding correction factors, allowing the use of maximum exposure durations while maintaining accuracy through parameter-based compensation.
2Measurement precision
If exposure duration is reduced to minimize motion measurement errors, then motion detection accuracy is improved, but video quality deteriorates severely
Solution Approach 1:
Rather than accepting reduced exposure as the only way to improve accuracy, the patent converts the trade-off relationship into a correction relationship. The system captures video at maximum exposure and uses the known exposure parameters to generate correction factors, thereby converting what was previously a harmful limitation into a manageable parameter that can be compensated for mathematically.
Solution Approach 2:
The patent applies preliminary anti-action by pre-calculating exposure correction factors based on the known exposure duration and timing before motion detection is performed. This preemptive correction approach prevents measurement errors from affecting the final results, allowing the system to use maximum exposure without suffering the previously inevitable accuracy degradation.
3Device complexity
If conventional motion detection methods are used without exposure correction, then system complexity is kept simple, but measurement accuracy deteriorates when motion duration approaches exposure duration
Solution Approach 1:
The patent applies preliminary action by extracting and storing exposure parameters (duration, timing, frame rate) before the actual motion detection process. These pre-extracted parameters are then used to calculate correction factors that are applied during motion analysis. This preliminary preparation of correction data allows the system to maintain simple processing during the main detection phase while still achieving high accuracy through pre-computed compensations.
Solution Approach 2:
The patent introduces an intermediary element - the exposure correction factor - that mediates between the raw video data and the final motion measurement. This correction factor acts as a bridge that translates the known exposure parameters into actionable adjustments for the motion detection algorithm, adding minimal complexity while significantly improving accuracy.
Data Source
AI summary
A system and method for detecting, quantifying, and/or measuring motion of an object and correcting for exposure includes providing a processor and at least one video sensor; determining video recording parameters of the at least one video sensor; recording, by the at least one video sensor, video of the object; extracting a data set from the video wherein the data set describes the motion of the object; calculating a frequency transform of the data set for at least one frequency; and performing exposure correction at the at least one frequency based on the recording parameters.


