Gradient Quantization for Real-Time Entity Detection
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Solution Overview
Problem
Existing image detection methods, such as those using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) techniques, face challenges in reducing false positives/negatives while meeting real-time processing constraints due to increased computational load with higher image resolution, limiting their practical application in safety and security systems.
Innovation Solution
The method involves quantizing the angle of gradients using predetermined partitioning elements and computing the projection of the gradient modulus, reducing the number of floating-point operations and circuit components required, thereby speeding up image processing and reducing spatial occupation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image resolution is increased to reduce false positives/negatives and improve entity detection accuracy, then measurement precision is improved, but computational load increases
Solution Approach 1:
The gradient angle calculation is segmented into discrete bins (e.g., 9 bins for 0-180 degrees). Instead of calculating continuous gradient angles for all pixels, the method divides the angle range into discrete segments and counts gradient orientations within each bin, reducing computational complexity while preserving detection accuracy
Solution Approach 2:
The method changes the parameter representation from continuous gradient angles to discrete bin indices. By transforming the continuous angle parameter into discrete bin categories (0-8 for 0-180 degrees), the computational load is reduced while maintaining the essential information needed for accurate entity detection
2Productivity
If image resolution is increased to process more image points per time unit, then productivity is improved, but computational load increases
Solution Approach 1:
The gradient orientation histogram is segmented into discrete bins, allowing parallel counting operations. Each bin can be populated independently by processing multiple pixels simultaneously, enabling higher throughput without proportionally increasing computational complexity
Solution Approach 2:
The method replaces complex trigonometric calculations (arctan, sine, cosine) with simple integer comparisons and increment operations. Instead of computing actual angle values using floating-point arithmetic, the algorithm determines which bin the gradient falls into and increments the corresponding counter, dramatically reducing computational load
3Measurement precision
If HOG/SVM-based methods are used for high-resolution image processing, then measurement precision is improved, but time consumption increases
Solution Approach 1:
The gradient magnitude and orientation are pre-computed for each pixel, and the orientation is immediately binned without waiting for subsequent processing steps. This preliminary binning allows parallel accumulation of histogram data across all pixels, reducing overall processing time while maintaining accuracy
Solution Approach 2:
Floating-point trigonometric functions are replaced with integer-based bin indexing. The gradient angle is mapped to discrete bin indices using simple arithmetic operations instead of computationally expensive arctan and trigonometric calculations, significantly reducing processing time
Data Source
AI summary
An apparatus and a method are provided for detecting entities in a numerical image, wherein the apparatus includes a computing unit configured for detecting, based on a histogram vector determined on the basis of gradient and partitioning information, the presence of at least one of the entities in the image, a signaling unit in signal communication with the computing unit, and configured for being activated when the computing unit detects the presence of at least one of the entities in the image, memory containing partitioning information, and configured for allowing access to the partitioning information on the basis of the gradient information, wherein each piece of partitioning information identifies at least one of the partitioning elements that allow the computing unit to quantize the gradient information.