Composite Frequency Spectrum Analysis for Video Motion Measurement
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Solution Overview
Problem
Existing video measurement systems face challenges in efficiently screening millions of pixels for significant frequencies and determining their spatial locations, making it difficult to analyze complex scenes with multiple frequency components.
Innovation Solution
A composite frequency spectrum graph or table is constructed by combining data from all pixels, using methods like linear averaging or peak hold averaging, to highlight significant frequencies and their spatial locations, allowing for interactive color mapping to enhance visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video measurement systems are used to capture motion from millions of pixels, then measurement precision and information quantity are improved, but data complexity and analysis difficulty increase significantly
Solution Approach 1:
The patent segments the complex data analysis task into distinct processing stages: (1) capturing video data from millions of pixels, (2) processing individual pixel data to extract motion information, (3) aggregating pixel-level data into region-level data, and (4) analyzing aggregated data to identify significant frequencies. This segmentation transforms an overwhelming single-step analysis into manageable sequential steps, resolving the contradiction between high measurement precision and data analysis complexity.
Solution Approach 2:
The patent introduces intermediate data structures and processing layers between raw pixel data and final analysis results. Specifically, it creates intermediate representations such as processed pixel data, region-of-interest data, and aggregated frequency spectra. These intermediaries serve as mediators that simplify the transition from millions of raw data points to meaningful frequency analysis, reducing the complexity burden while preserving measurement precision.
2Measurement precision
If manual comparison of dynamic data at each location is performed, then measurement accuracy is maintained, but time consumption and productivity decrease
Solution Approach 1:
The patent merges data from multiple pixels and locations into aggregated frequency spectrum data. Instead of manually comparing data at each individual location, the system combines pixel-level frequency spectra into region-level spectra, and further aggregates these into overall frequency spectrum data. This merging process maintains measurement accuracy by preserving frequency information while dramatically improving productivity by reducing the number of comparisons needed from millions of individual pixel comparisons to a manageable set of aggregated frequency analyses.
Solution Approach 2:
The patent creates universal processing algorithms that can analyze frequency spectra from any number of pixels, locations, or regions using the same methodology. The frequency analysis functions and data aggregation procedures are designed to be universally applicable regardless of scene complexity or data volume, enabling efficient scaling from simple to complex analyses while maintaining consistent accuracy standards.
3Loss of information
If frequency spectrum data from all pixels is analyzed individually, then complete frequency information is captured, but data volume and processing time increase exponentially
Solution Approach 1:
The patent performs preliminary frequency spectrum analysis at the pixel level before aggregation. By calculating frequency spectra for individual pixels in advance and storing these results, the system avoids redundant calculations during the aggregation phase. This preliminary action preserves complete frequency information from all pixels while significantly reducing processing time during the subsequent aggregation and analysis stages, as the computationally intensive FFT operations are performed once per pixel rather than repeatedly during aggregation.
Solution Approach 2:
The patent changes the parameter representation from individual pixel coordinates and intensities to frequency domain parameters. By transforming spatial-temporal pixel data into frequency spectra, the system compresses the information representation while preserving all significant frequency content. This parameter transformation enables efficient aggregation because frequency parameters from multiple pixels can be combined using straightforward mathematical operations rather than processing raw pixel sequences.
Data Source
AI summary
Systems and methods are provided to evaluate moving objects undergoing periodic motion through the screening of a video recording of such objects in motion for frequency peaks in the spectral data, and to determine spatially where these frequencies occur in the scene depicted in the video recording, wherein a frequency spectrum is created for a subset of pixels or virtual pixels and a composite frequency spectrum table or graph is constructed of frequencies that are selected from among the larger group of frequencies represented by the frequency peaks of the spectral data.