Vehicle Plate Matching for Speed Detection
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Solution Overview
Problem
Existing traffic monitoring systems face challenges in accurately identifying vehicles exceeding speed limits due to issues like varying illumination, different plate designs across countries, and noise in images, leading to unreliable vehicle identification.
Innovation Solution
The Plate Matching (PM) technology converts alphanumeric characters from vehicle plates into a new representation space, allowing for correlation-based identification of vehicles across different locations and lighting conditions, without requiring specific algorithms for each country's plate type, and associates a correlation score to determine if the vehicle has exceeded speed limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional vehicle identification systems are used, then vehicle identification can be achieved, but identification reliability deteriorates due to varying illumination, different plate designs across countries, and noise in images
Solution Approach 1:
The patent introduces an intermediary representation space that mediates between the original plate images and the final vehicle identification. Instead of directly recognizing plates with complex algorithms, the system converts plate images into a simplified representation space where vehicles are identified through correlation of these representations, eliminating the need for country-specific recognition algorithms while improving reliability under varying conditions
Solution Approach 2:
The patent changes the representation parameters of vehicle plates by transforming them into a new representation space. This parameter transformation simplifies the identification process by converting complex visual patterns into a standardized format that can be correlated across different countries and lighting conditions, thereby improving identification reliability without increasing algorithmic complexity
2Measurement precision
If precise plate identification is attempted, then identification accuracy may improve, but the system becomes more complex and less adaptable to different country plate typologies
Solution Approach 1:
The patent creates a universal representation space that can handle multiple country plate typologies with a single algorithm. By transforming plates from different countries into the same representation space, the system achieves multi-functionality without requiring separate recognition algorithms for each country, thereby improving both accuracy and adaptability simultaneously
3Reliability
If average speed monitoring is implemented, then speed limit violation detection improves, but false positives increase due to temporary speed variations during overtaking maneuvers
Solution Approach 1:
The patent performs preliminary actions by capturing images at multiple points along the road and using correlation to establish temporal and spatial relationships between these points. This preliminary data collection allows the system to distinguish between temporary speed variations (like overtaking) and sustained speeding by analyzing the pattern of images across different locations and times, thereby reducing false positives while maintaining detection reliability
Data Source
AI summary
The invention relates to a method and a system for identifying moving objects by employing a tag, said tag comprising at least alphanumeric characters and said tag being extracted from pictures taken by cameras located in at least two different points within a certain distance comprising extracting alphanumeric characters of said tag from the pictures taken by at least two cameras; converting said alphanumeric characters into other new characters of another representation space; creating a string of said new characters for each of the tags extracted from the pictures taken by the cameras at different locations, said cameras being synchronized and said pictures taken by the cameras within a predetermined time interval; comparing the strings by associating a correlation score; inputting a threshold score; identifying the moving object if the correlation score is over the predetermined threshold score.