Image Frame Alignment for High-Precision Motion Detection
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Solution Overview
Problem
Manual identification of image frames containing activity of interest in video recordings is inefficient, labor-intensive, and prone to subjectivity, omission, and error, especially when dealing with large sequences of images.
Innovation Solution
A system that aligns and transforms image frames to generate difference images, computes motion values, and determines activity of interest by normalizing for spatial and temporal jitter, using spectral and spatial transformations to isolate objects of interest and de-emphasize background noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual examination is used to identify image frames containing activity of interest, then detection accuracy can be maintained through human judgment, but productivity decreases due to time and labor consumption
Solution Approach 1:
The patent replaces manual visual examination with an automated computational system that processes image frames through spectral transformation, difference image generation, and motion value computation. This substitution eliminates human labor while maintaining detection capability through algorithmic analysis of motion patterns in image sequences.
Solution Approach 2:
The system enables self-service detection by automatically processing image frames without human intervention. The computational workflow independently performs spectral transformation, generates difference images, computes motion values, and identifies frames containing activity of interest, making the detection process autonomous and scalable.
2Reliability
If manual identification process is used, then subjectivity and error can be introduced by human observers, but the process remains simple and easy to implement
Solution Approach 1:
The patent transforms image frames from spatial domain to spectral domain through spectral transformation, changing the representation parameters to emphasize motion patterns. This parameter change enables consistent, objective detection by converting visual information into quantifiable spectral characteristics that can be processed uniformly across all frames.
Solution Approach 2:
The detection process is segmented into distinct computational stages: spectral transformation of individual frames, generation of difference images by subtracting transformed frames, computation of motion values from difference images, and threshold-based identification of active frames. This segmentation creates a systematic, repeatable workflow that eliminates subjectivity.
3Measurement precision
If spectral and spatial transformations are applied to isolate objects of interest, then measurement precision of motion detection is improved, but device complexity increases due to additional processing steps
Solution Approach 1:
The patent extracts motion information from image frames by applying spectral transformation to separate objects of interest from the background. This extraction process isolates the relevant motion signals while filtering out stationary or less significant elements, improving detection precision by focusing computational attention on salient features.
Data Source
AI summary
A first image is aligned with a second image. A first motion value is computed based at least in part on a sum of differences between corresponding pixels in the first image and the second image. A second motion value is computed further based at least in part on a third image. An impact score is generated based at least in part on a difference between values derived from the first motion value and the second motion value and an action is performed depending at least in part on whether the impact score indicates that damage has occurred among objects represented in the first image and the second image.