Vehicle Identification System Using Image Feature Extraction
Solution Overview
Problem
Current methods for identifying vehicles and individuals at fueling stations are inefficient, as they rely on manual recognition by patrons or attendants, leading to delayed responses and potential missed opportunities to prevent losses.
Innovation Solution
A system that uses a camera to capture images of vehicles and individuals at fueling stations, processes the images to extract features such as vehicle make/model, license plate number, and facial features, and then queries a remote database to retrieve identification information, which is used to generate alerts for end-user devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual recognition by patrons or attendants is used to identify vehicles and individuals, then device complexity is reduced, but response time increases and productivity decreases
Solution Approach 1:
The patent replaces the manual mechanical recognition system (patrons or attendants visually identifying vehicles) with an automated optical system using cameras and image processing algorithms. The camera captures images of vehicles and individuals, which are then processed by a server to extract identifying features and compare against databases, enabling automated identification without human intervention.
Solution Approach 2:
The system enables self-service identification where the automated image recognition and database matching processes occur without requiring human operators. The server automatically processes captured images, extracts features, queries databases, and generates alerts, making the identification system autonomous and eliminating the need for manual recognition by station personnel.
2Device complexity
If manual recognition methods are used, then device complexity is lower, but loss prevention capability deteriorates due to delayed response
Solution Approach 1:
The system performs preliminary identification and verification actions automatically as vehicles and individuals approach or enter the fueling station. By capturing images and processing them through the recognition system before potential loss events occur, the system can proactively identify stolen vehicles, verify customer identities, and alert authorities in advance, preventing losses rather than reacting to them after discovery.
Solution Approach 2:
The system implements continuous feedback loops where captured images are processed, compared against databases of stolen vehicles and known individuals, and results are fed back to station personnel and law enforcement. This real-time feedback mechanism ensures that identification information is immediately available for loss prevention actions, maintaining high reliability while using automated systems.
3Measurement precision
If automated image processing is implemented, then identification accuracy improves, but device complexity increases
Solution Approach 1:
The automated identification system is segmented into distinct functional modules: image capture by cameras, feature extraction by image processing algorithms, database querying by the server, and alert generation by the control system. This segmentation allows each component to be optimized independently for its specific function while maintaining overall system accuracy, managing complexity through modular architecture rather than a monolithic system.
Data Source
AI summary
Systems, devices, and methods for associating vehicle identification information with other visually captured information are provided. In general, a system can include a processor configured to receive an image from a camera located at a fueling station. The processor can be configured to apply an algorithm to extract various features from the image, such as a vehicle that is present in the image, facial features of a driver and/or a passenger of the vehicle, or a license plate of the vehicle. The features can be used by the processor to determine information from the features, such as the identity of the driver, the make/model of the vehicle, or an owner of the vehicle. The processor can then, based on the determined information, be configured to take a variety of actions and thereby reduce the likelihood of possible future losses associated with the vehicles or individuals present in the image.