LPR Error Correction via Spatiotemporal Pattern Analysis
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Solution Overview
Problem
Existing automatic license plate recognition (LPR) technologies suffer from inaccuracies due to malfunctions, weather conditions, and obstructions, leading to unreliable data and costly manual verification processes, especially in large-scale intelligent transportation systems.
Innovation Solution
A fully data-driven system using machine learning to analyze LPR data and detect misidentified license plate numbers by learning driving patterns and error patterns across multiple LPR sensors, without the need for additional sensors or manual intervention, employing a similarity measurement and error pattern probability matrix for correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual testing and observation are used to detect inaccurate LPR results, then detection accuracy improves, but time consumption and resource requirements increase significantly
Solution Approach 1:
The system enables automatic self-detection of inaccurate LPR results by using machine learning models that analyze LPR data patterns, driving patterns, and spatiotemporal characteristics without requiring manual intervention. The system processes and diagnoses its own data accuracy issues autonomously
Solution Approach 2:
The patent replaces manual mechanical testing and observation with an automated computational system using machine learning algorithms, error pattern probability matrices, and data-driven analysis to detect and diagnose inaccurate LPR results
2Reliability
If additional sensors or manual intervention are employed to verify LPR accuracy, then measurement reliability improves, but device complexity and cost increase
Solution Approach 1:
The system uses existing LPR data and driving pattern information to automatically verify accuracy without requiring additional external sensors or manual verification processes
Solution Approach 2:
The patent introduces computational intermediaries including error pattern probability matrices, driving pattern models, and machine learning algorithms that mediate between raw LPR data and accuracy verification, eliminating the need for physical additional sensors
3Measurement precision
If manual testing is performed on every camera and sensor, then detection completeness improves, but productivity and scalability deteriorate
Solution Approach 1:
The patent replaces manual testing procedures with automated machine learning-based detection that can process millions of LPR records simultaneously across thousands of cameras and sensors, maintaining complete detection coverage while dramatically improving processing efficiency
Solution Approach 2:
The system performs selective detection focusing on identifying inaccurate results through data pattern analysis rather than exhaustive manual testing of every sensor, achieving effective detection coverage with higher processing efficiency
Data Source
AI summary
Disclosed herein are system, method, and computer-readable device embodiments for automatically correcting erroneous license plate numbers generated by automatic license plate recognition. An embodiment operates by selecting a set of adjacent license plate recognition (LPR) stations comprising a first LPR station, a second LPR station, and a third LPR station, accessing an error pattern probability matrix for at least the third recognized license plate number accessed in relation to at least one of the first recognized license plate number or the second recognized license plate number differing from the third recognized license plate number, and determining a corrected license plate number based on the error pattern probability matrix. In some embodiments, LPR correction may be accurately realized even when an erroneous license plate numbers are missing characters, such as by visual obstruction from an LPR camera or sensor, or has no characters in common with the real license plate number.