Vehicle Venue Entry Payment Through License Plate Recognition
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Solution Overview
Problem
Current entry systems for motor vehicles into fee areas require gated entry and manual labor, leading to congestion, long waiting times, health risks during epidemics, and unreliable license plate recognition, with state-of-the-art systems operating at low to mid-ninety percent recognition rates and failing to address issues like lighting, weather conditions, and plate obscurity.
Innovation Solution
A system utilizing a camera for recognizing vehicles and license plates, coupled with machine learning and a venue app, allows vehicles to enter without gates or attendants by storing images and using machine learning to match partial plate reads, enabling easy identification through high-resolution photo arrays, and using LPC at exit points to track occupancy and prevent revenue loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual entry booths with attendants are used, then payment collection is reliable, but congestion and waiting time increase significantly
Solution Approach 1:
The patent replaces the mechanical manual entry booth system with an automated license plate recognition system using cameras and image processing technology. The system captures images of license plates, processes them through recognition algorithms, and automatically identifies vehicles without requiring manual intervention, thereby eliminating waiting time while maintaining payment reliability through automated enforcement.
Solution Approach 2:
The system enables self-service entry by automatically reading license plates and identifying vehicles without requiring driver action or attendance. The automated recognition and payment system serves itself by processing vehicles through image capture and algorithmic identification, eliminating the need for human attendants while maintaining reliable payment collection.
2Reliability
If gated entry with manual labor is used, then payment can be collected, but operational costs increase due to staff requirements
Solution Approach 1:
The patent substitutes human labor with automated image recognition technology. Cameras capture license plate images, and processing systems automatically identify vehicles and enforce payment, eliminating the need for paid staff while maintaining reliable payment collection. This technological substitution directly reduces operational energy costs associated with human workers.
3Extent of automation
If traditional license plate reading systems are used, then entry automation is achieved, but recognition rate remains low at 75-85 percent
Solution Approach 1:
The patent transforms the license plate recognition approach by changing key parameters: using high-resolution digital imaging instead of traditional optical reading, applying advanced image processing algorithms, and utilizing multiple capture angles. These parameter changes enable the system to achieve near 100% recognition accuracy by processing images through computational algorithms rather than traditional optical character recognition.
Solution Approach 2:
The system performs preliminary image capture and processing before final recognition. Multiple images are captured in advance, processed through algorithms to enhance quality and extract license plate information, and stored for verification. This preliminary action ensures high recognition rates by preparing data before the final identification decision is made.
4Measurement precision
If LPC systems operate in controlled environments like gated communities, then recognition rate reaches 97 percent, but this performance cannot be replicated in open venue environments
Solution Approach 1:
The patent creates a universal license plate recognition system that functions effectively across diverse environments. The system uses robust image processing algorithms that adapt to varying lighting conditions, weather, and camera angles. By employing multiple capture points and advanced processing techniques, the system achieves high recognition rates in both controlled and open venue environments, making it environmentally versatile.
Solution Approach 2:
The system adjusts operational parameters based on environmental conditions. It modifies image capture settings, processing algorithms, and recognition thresholds to accommodate different lighting, weather, and spatial conditions. This parameter adaptation enables the system to maintain high recognition rates whether in controlled gated communities or open public venues.
Data Source
AI summary
There is a system for admitting automobiles to a venue comprising: at least one camera for recognizing an automobile, and a license plate. A server is configured for receiving a picture of the automobile and a picture of the license plate of the automobile. Furthermore, there is at least one database for storing, matching and identifying the picture of the automobile and the license plate. In addition, there is at least one screen for allowing a user to identify their automobile taken from a list of automobiles. There is also a process for tracking an automobile within an area which comprises the steps of identifying a pre-defined area and identifying an automobile for screening into the area. Next the auto and its license plate are photographed. This information is stored in a database and then used to generate invoices and track the location of these automobiles. Customer can access the database, enter the license plate of his vehicle, the database recognizes his vehicle, the customer makes payment for his vehicle, and the app places the customer information onto the PAID category list on the database.