ALPR Vehicle Identification Profiles for Read Accuracy
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Solution Overview
Problem
Existing ALPR systems face challenges in accurately identifying license plate characters due to various design elements, dirt, damage, or obstructions, leading to varying read accuracy across different OCR engines.
Innovation Solution
A system and method that captures vehicle images, processes them to identify license plate numbers and vehicle descriptors, determines a probability value for the read, and updates a vehicle identification profile in a database when the probability exceeds a threshold, using a combination of OCR and feature recognition engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple OCR engines are used to improve read accuracy, then the reliability of license plate recognition is improved, but the device complexity and processing time increase
Solution Approach 1:
The patent combines multiple OCR engines with a vehicle identification profile system into a unified ALPR system. The system merges the outputs of multiple OCR engines and cross-references them with vehicle descriptor data from image analysis, creating a integrated approach that improves reliability while managing complexity through systematic integration.
Solution Approach 2:
The system implements feedback mechanisms where the vehicle identification profile (containing vehicle descriptors, make, model, year) is used to verify and correct OCR readings. The system cross-references license plate readings with vehicle identification data, providing a feedback loop that enhances read accuracy by validating results against multiple data sources.
2Measurement precision
If image processing is enhanced to identify vehicle descriptors, then the measurement precision of vehicle identification is improved, but the use of energy and processing time increase
Solution Approach 1:
The system segments the image processing task into distinct components: license plate detection and character recognition, vehicle descriptor extraction (color, shape, distinctive features), and profile matching. This segmentation allows each component to be optimized independently, improving measurement precision while managing energy consumption through targeted processing.
Solution Approach 2:
The system performs preliminary image processing to extract vehicle descriptors (color, shape, distinctive features) and creates a vehicle identification profile before attempting full vehicle identification. This preliminary action prepares data structures that speed up subsequent matching operations, reducing the energy required for complete identification.
3Reliability
If the system stores and processes vehicle identification profiles, then the reliability of vehicle identification is improved, but the quantity of data and storage requirements increase
Solution Approach 1:
The system extracts only the essential identifying features from vehicle images and stores them as structured data in identification profiles (license plate number, vehicle descriptors, make, model, year). By extracting and storing only the critical identification data rather than complete image archives, the system improves identification reliability while minimizing data volume and storage requirements.
Data Source
AI summary
Embodiments herein provide various systems and methods for automated classification of vehicle reads to build vehicle identification profile and for automated vehicle identification using content extracted from an image frame of a vehicle to identify a most probable vehicle identification profile. An example method comprises capturing, by a camera, a read comprising an image frame including a portion of a vehicle; identifying at least one of a license plate number and a descriptor of the vehicle using image processing on the read; determining a probability value that the read includes the vehicle based on the identified at least one of the license plate number and the descriptor; when the probability value exceeds a threshold value, identifying a vehicle identification profile in a database using the identified at least one of the license plate number and the descriptor; and updating the vehicle identification profile to include the captured read.