SNoW Classifier Context Window Reduction via T-Score Ranking
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Solution Overview
Problem
The existing Automated License Plate Recognition (ALPR) systems face challenges in high-speed and high-bandwidth applications due to the large context size of the SNoW classifier, which results in significant real-time computation requirements, limiting their usefulness in heavy traffic scenarios.
Innovation Solution
The method determines the most significant locations within the classifier window by calculating the mean and standard deviation of every pixel location for both positive and negative samples, using t-scores and mean differences to rank-order the importance of pixel locations, allowing for a reduced context window that achieves a detection rate close to the full classifier, while reducing computation overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the full SNoW classifier with large context size is used, then detection accuracy is maintained, but real-time computation time increases significantly
Solution Approach 1:
The patent segments the classifier context window into multiple regions and identifies only the most important regions for detection. By dividing the full context window and selectively processing only significant segments, the system maintains detection accuracy while reducing computation time in real-time applications.
Solution Approach 2:
The patent extracts and identifies the most important locations within the classifier context window using training data analysis. By taking out only the critical regions that contribute most to detection accuracy and discarding less important areas, the system achieves fast detection with reduced computation while preserving essential detection performance.
2Reliability
If the full classifier context window is processed, then detection rate is maximized, but computation overhead and storage requirements increase
Solution Approach 1:
The patent performs preliminary analysis during the training phase to identify and mark the most important locations within the classifier context window. This preliminary action creates a map of critical regions that can be directly applied during real-time detection, eliminating the need to process the entire context window and significantly reducing computation overhead while maintaining detection rate.
Solution Approach 2:
The patent applies partial action by processing only the most important locations identified during training rather than the full context window. By focusing computational resources on the essential regions that provide the majority of detection value, the system achieves high detection rates with substantially reduced computation overhead and storage requirements.
3Reliability
If the large context size is used for classification, then detection performance is improved, but processing speed in high-bandwidth applications decreases
Solution Approach 1:
The patent applies local quality by assigning different importance weights to different locations within the classifier context window. Instead of uniformly processing all regions, the system identifies and focuses computational effort on locally important regions that have higher quality or contribution to detection, thereby improving processing speed without sacrificing overall detection performance.
Solution Approach 2:
The patent changes the parameter of context window size dynamically by using the full context during training to identify important locations, then using a reduced context window during real-time detection that includes only the most important locations. This parameter change enables fast processing in high-bandwidth applications while preserving detection performance through selective region processing.
Data Source
AI summary
Methods, systems and processor-readable media for determining, post training, which locations of a classifier window are most significant in discriminating between class and non-class objects. The important locations can be determined by calculating the mean and standard deviation of every pixel location in the classifier context for both the positive and negative samples of the classifier. Using a combination of t-scores and mean differences, the importance of all pixel locations in the classifier score can be rank ordered. A sufficient number of pixel locations can then be selected to achieve a detection rate close enough to the full classifier for a particular application.