Vehicle Identification Profile Generation at Edge
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Solution Overview
Problem
Automatic license plate recognition (ALPR) systems face challenges in accurately detecting and reading license plate characters due to various design elements, environmental conditions, and plate damage, leading to reduced read accuracy.
Innovation Solution
The implementation of a system that utilizes an optical character recognition (OCR) engine and a feature recognition and classification engine to identify characters on a license plate and build a vehicle identification profile, incorporating metadata for enhanced accuracy and confidence in readings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR engines are used to read license plate characters, then the system is simple and fast, but the read accuracy decreases when plates have designs, pictures, or are obscured by dirt, snow, or damage
Solution Approach 1:
The patent segments the license plate reading task into multiple specialized components: a primary OCR engine for standard plates, a secondary OCR engine for alternative character sets, and a vehicle description generator. Each component handles specific cases, improving overall accuracy without requiring a single complex system to handle all scenarios.
Solution Approach 2:
The patent introduces an intermediary vehicle description generation system that creates textual descriptions of vehicles (color, make, model, year) as metadata. This intermediary layer supplements the OCR reading process, allowing the system to cross-reference and verify plate readings against vehicle characteristics, thereby improving accuracy when plates are obscured or damaged.
2Measurement precision
If multiple OCR engines with different algorithms are used to improve read accuracy, then the recognition accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent implements a dynamic, adaptive processing system that adjusts the level of analysis based on image quality and confidence levels. The system starts with primary OCR processing and only engages additional OCR engines or vehicle description generation when the primary reading confidence is below a threshold, optimizing processing time while maintaining accuracy.
Solution Approach 2:
The system incorporates feedback loops where vehicle description metadata is generated and used to verify or correct OCR readings. The confidence levels from initial processing feed into decisions about whether additional processing is needed, creating an efficient feedback-driven approach that balances accuracy with processing time.
3Adaptability or versatility
If vehicle description metadata is collected and stored in profiles, then the ability to match and search vehicles improves, but the data storage and processing requirements increase
Solution Approach 1:
The patent creates multi-functional vehicle profiles that serve multiple purposes: storing vehicle description metadata for future reference, enabling cross-validation of license plate readings, providing alternative identification methods when plates are obscured, and supporting various search and matching operations. This universal data structure maximizes the utility of stored information while avoiding redundant data collection.
Data Source
AI summary
A method for generating a vehicle identification profile and building a vehicle identification profile database. The method may be executed at an edge of a networked system. The method identifies at least one of a number of characters on a license plate and one or more alphanumeric descriptors. The alphanumeric descriptors are obtained from physical or visual features or characteristics of a vehicle, as identified from a video stream. A vehicle profile including the alphanumeric descriptors and the one of a number of license plate characters is generated.