Vehicle Vision System Geo-Referenced Feature Matching
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Solution Overview
Problem
Current vehicle vision systems struggle to reliably identify specific Points of Interest (POIs) such as buildings or landmarks, lacking robustness and speed in object recognition, especially under varying environmental conditions like lighting and viewing directions.
Innovation Solution
A vehicle vision system that employs a camera and processor to capture images, determine feature descriptors, and match them with geo-referenced feature descriptors from annotated map data, enabling robust and fast object recognition by utilizing geo-referenced feature vectors and position information for accurate orientation and positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object recognition methods are used, then classification into limited classes is reliable, but identification of specific POIs such as buildings or landmarks is not possible
Solution Approach 1:
The system segments the object recognition task into two parts: using traditional classification methods for general object identification and using feature descriptor matching with annotated map data for specific POI identification. This allows the system to maintain reliability for common objects while extending capability to identify specific landmarks and buildings.
Solution Approach 2:
The system uses pre-computed feature descriptors and annotated map data that are prepared in advance. By storing geo-referenced feature descriptors in the map data before the actual recognition task, the system enables fast and accurate POI identification without requiring real-time training or complex processing during execution.
2Measurement precision
If neural networks are trained for specific POI recognition, then identification accuracy improves, but system complexity and training requirements increase
Solution Approach 1:
Instead of training a complex neural network from scratch for specific POI recognition, the system uses copies of pre-computed feature descriptors that are extracted from images and matched against annotated map data. This approach achieves high POI identification accuracy without requiring complex training procedures or large computational resources.
Solution Approach 2:
The system uses simple feature descriptor matching rather than complex neural networks. The feature descriptors act as lightweight, disposable representations that can be quickly extracted and matched, providing sufficient accuracy for POI identification without the overhead of trained neural network models.
3Reliability
If feature descriptors are used for robust recognition, then robustness against varying lighting and viewing directions improves, but processing speed decreases
Solution Approach 1:
The system pre-computes and stores feature descriptors in the annotated map data before the actual recognition task. By having these descriptors ready in advance, the system can quickly match them against extracted features from images, achieving both robustness under varying conditions and fast processing speeds without requiring complex real-time computation.
Data Source
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AI summary
A vision system (1) comprises a camera (5) configured to capture an image (15) and a processor (2) coupled to the camera (5) and configured to determine at least one feature descriptor for the image (15). The processor (2) has an interface (3) to access annotated map data which includes geo-referenced feature descriptors. The processor (2) is configured to perform a matching procedure between the at least one feature descriptor determined for the at least one image (15) and the retrieved geo-referenced feature descriptors.