Geolocated Database for Traffic Sign Disambiguation
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Solution Overview
Problem
Existing driving monitoring systems face challenges in accurately identifying traffic control devices, particularly U-turn signs, due to variability in placement and subtle visual differences, which leads to false alarms and reduced precision in detecting U-turn maneuvers.
Innovation Solution
A method involving a geolocated database system that uses processor-based image analysis to detect traffic control devices, determine vehicle location, and query a database to accurately classify U-turn signs, improving precision and recall in challenging scenarios by distinguishing between visually similar signs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual detection methods are used to identify traffic control devices, then the system can detect signs in real-time, but the precision is reduced due to subtle visual differences between similar signs
Solution Approach 1:
The patent introduces a database as an intermediary between the visual detection system and the final identification decision. The system captures images of traffic control devices, extracts features, and queries a database containing reference images and metadata (location, sign type, regulatory information). This intermediary database resolves ambiguities by providing contextual information that goes beyond visual appearance alone, thereby improving identification precision despite visual similarities between signs.
Solution Approach 2:
The patent transitions from relying solely on two-dimensional visual appearance to incorporating additional dimensions of information including geographic location (GPS coordinates), temporal context, and database-matched metadata. By adding these dimensional layers beyond simple image matching, the system can distinguish between visually similar signs based on their contextual attributes, resolving the precision problem caused by subtle visual differences.
2Measurement precision
If the system queries a database for every detected traffic control device, then identification accuracy improves, but processing time increases
Solution Approach 1:
The patent implements preliminary actions by pre-processing and storing comprehensive metadata about traffic control devices in the database before they are needed for identification. The database is pre-populated with location information, sign types, regulatory details, and image references. When a device is detected, the system only needs to query using extracted features and location data, rather than performing complex analysis from scratch, thereby reducing query processing time while maintaining high accuracy.
Solution Approach 2:
The patent applies local quality by making the database query process adaptive to local conditions. The system queries the database with location-specific parameters and retrieves only relevant results for the detected device's geographic context. This localized querying approach, rather than exhaustive searches, reduces processing time while maintaining identification accuracy by focusing computational resources on relevant matches.
3Reliability
If the system distinguishes between visually similar traffic signs, then false alarms are reduced, but the complexity of the detection system increases
Solution Approach 1:
The patent merges multiple detection and verification functions into a unified system. The visual detection module, feature extraction module, database query module, and verification module work together as an integrated system. By combining these functions rather than treating them as separate complex subsystems, the overall system manages complexity while achieving reliable distinction between visually similar signs through the coordinated action of each component.
Solution Approach 2:
The patent uses copying by storing reference images and metadata from known traffic control devices in the database. When a device is detected, the system compares it against copied reference examples rather than relying on complex real-time analysis alone. This copying approach simplifies the detection process by leveraging pre-analyzed reference data, reducing system complexity while improving reliability in distinguishing similar signs.
Data Source
AI summary
Systems and methods are provided for classifying one or more images of video data captured by a camera in a vehicle. The classification involves a detection of a traffic control device, such as a U-Turn sign, in image data. The classification further involves querying a database with a location of the vehicle from which the traffic control device was observed. In some embodiments, the database contains entries for various types of traffic control devices from a family of traffic control devices, where each type of traffic control device in the family has a similar visual appearance. The database query may therefore help disambiguate the visually perceived traffic control device at a particular location. Embodiments may be incorporated into navigation or driver safety systems.