Image Analyzer Detecting Unsteady States via Multi-Block Statistical Analysis
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Solution Overview
Problem
Conventional image analysis systems are limited in their ability to detect various types of unsteady states in images, primarily relying on optical flows and failing to accurately identify complex unsteady conditions such as congestion, accumulation, and aggregation of objects.
Innovation Solution
The system sets multiple partial regions in an image, calculates optical flows, densities, and divides the image into blocks to derive statistical amounts like kinetic momentum, energy, and potential energy, which are used to specify unsteady blocks based on predetermined thresholds, thereby enhancing the detection of unsteady states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional optical flow methods are used to detect unsteady states, then the system is simple to operate, but the types of unsteady states that can be specified are limited
Solution Approach 1:
The patent divides the image into multiple blocks and further divides each block into partial regions, enabling multi-level analysis. This segmentation allows the system to detect various types of unsteady states (congestion, accumulation, aggregation, reverse movement, abnormal speed, turbulence) by analyzing different statistical amounts at different spatial scales, thereby increasing the types of detectable unsteady states without overwhelming complexity.
Solution Approach 2:
The patent introduces multiple dimensions of analysis by calculating various statistical amounts (density, moving amount, kinetic momentum, kinetic energy, potential energy) in addition to traditional optical flow. This multi-dimensional approach enables detection of diverse unsteady states by examining different physical properties simultaneously, expanding the system's adaptability while maintaining structured analysis.
2Measurement precision
If multiple statistical amounts are calculated for each block, then the precision of unsteady state detection is improved, but the computational load increases
Solution Approach 1:
The patent divides the image into blocks and further divides each block into partial regions, enabling distributed calculation of statistical amounts. By segmenting the analysis region and using parallel processing across multiple blocks and partial regions, the system achieves high detection precision through comprehensive statistical analysis while managing computational load through efficient division of work.
Solution Approach 2:
The patent calculates multiple statistical amounts (density, moving amount, kinetic momentum, kinetic energy, potential energy) for each block to comprehensively characterize unsteady states. This excessive calculation of statistical parameters ensures high detection accuracy by capturing various aspects of object behavior, with the benefit that not all statistical amounts need to be computed for every single case, allowing flexible resource allocation.
Data Source
AI summary
According to an embodiment, an image analyzer includes a set unit, a first calculator, a second calculator, a divider, a third calculator, and a specifier. The set unit sets multiple partial regions in the image. The first calculator calculates an optical flow in a partial region. The second calculator calculates the density of an object included in the partial region. The divider divides the image into the multiple blocks. The third calculator calculates, for a block, a statistical amount that is derived from the density and the moving amount of the object derived from the optical flow for the partial region included in the block. The specifier specifies the block, out of the blocks, as an unsteady block that is in an unsteady state on the basis of the statistical amount.