Vehicle and Asset Identification Using Visual Feature Tracking
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Solution Overview
Problem
Current methods for identifying and tracking vehicles and their assets at locations like ports or roadways are inefficient and prone to errors due to manual data entry, leading to congestion and missed delivery deadlines.
Innovation Solution
An automated system using high-resolution cameras and machine learning algorithms to capture and analyze vehicle characteristics, producing individualistic identifiers for accurate vehicle and asset tracking, with edge computing for real-time data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data entry by workers at gates is used to identify and monitor vehicles, then device complexity is reduced, but productivity is low and errors occur
Solution Approach 1:
The patent replaces manual mechanical data entry operations with an automated optical-mechanical system consisting of cameras, processors, and databases. The system automatically captures vehicle images, processes them through algorithms to extract identifying features, and stores the data without human intervention, thereby eliminating manual labor while maintaining system functionality.
Solution Approach 2:
The system enables vehicles to be automatically identified and tracked through their own visual characteristics without requiring human operators. The automated image processing system extracts distinguishing features directly from vehicle images and performs matching independently, allowing the system to serve itself without external human input for routine identification tasks.
2Measurement precision
If manual logging of vehicle information is used, then device complexity is low, but measurement precision is poor due to human errors
Solution Approach 1:
The patent replaces error-prone manual data entry and logging with an automated computer vision system. Cameras capture high-resolution vehicle images, and processing algorithms automatically extract and analyze identifying features such as license plates, vehicle models, and unique markings, eliminating human errors in data recording and improving identification accuracy.
Solution Approach 2:
The system creates accurate digital copies of vehicle identifying information through image capture and processing. Instead of manual transcription, the system digitally reproduces vehicle characteristics from visual data, storing precise copies in a database that can be reliably referenced and compared without the errors associated with human handwriting or data entry.
3Productivity
If automated image capture and analysis systems are deployed, then productivity and accuracy improve, but device complexity increases
Solution Approach 1:
The patent divides the vehicle identification system into distinct functional segments: image capture modules (cameras), processing modules (computers with algorithms), storage modules (databases), and output modules (displays). Each segment performs a specific function independently, allowing the complex system to be managed through modular components that can be developed, maintained, and scaled separately.
Solution Approach 2:
The system introduces a computer processing system as an intermediary between the physical vehicle and the digital record. The computer receives raw images from cameras, processes them through algorithms to extract meaningful data, and generates standardized output information. This intermediary layer manages the complexity by transforming unstructured visual data into structured, usable information.
4Loss of time
If real-time vehicle monitoring is implemented, then loss of time is reduced, but use of energy increases
Solution Approach 1:
The system implements periodic or event-triggered image capture rather than continuous monitoring. Cameras capture vehicle images at specific intervals or when vehicles enter/exit monitored zones, and processing occurs on-demand. This approach maintains real-time monitoring capability for critical events while reducing energy consumption during periods of low activity.
Solution Approach 2:
The system prioritizes and accelerates processing of critical real-time events while skipping or delaying less urgent operations. When a vehicle enters a monitored zone or triggers an event, the system rapidly captures and processes images immediately. During normal periods, processing can be deferred or performed at lower intensity, reducing energy consumption while maintaining responsiveness to important events.
Data Source
AI summary
A method and system accurately identify vehicles during transit and may be used for tracking the vehicle. Images of discrete distinguishing characteristics of a vehicle, respectively, are captured at an unmanned monitoring station. The images are collectively analyzed to produce an individualistic identifier. The individualistic identifier is correlated with data representative of a particular vehicle stored as part of a database of vehicles. Accordingly, the particular vehicle present at the monitoring station can be identified so that the vehicle can be tracked. Furthermore, key assets of the vehicle, such as the tractor, chassis, and cargo box of a truck, can be identified during the process and by the system so that they can be tracked independently of one another.