Stereo Vision Object Recognition for Road Signs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing object detection and recognition systems face challenges in accurately identifying road signs in complex images from moving vehicles, due to variations in size, orientation, color, and similarity to other objects, and require large databases for machine learning, which is time-consuming and expensive, and struggle with real-time processing and environmental factors like light intensity.
Innovation Solution
An object detection and recognition system using multiple image capture devices to form stereoscopic representations, isolating objects with range information, and employing pixel mapping, least squares curve fitting, and correlation analysis to identify objects of interest, with templates pre-screened for location, geometry, and color, and utilizing GPS for locale-specific updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning algorithms with large image databases are used for road sign recognition, then recognition capability is improved, but system cost and time consumption increase
Solution Approach 1:
The system pre-processes and stores road sign templates in a database before actual recognition operations. These templates include various orientations, sizes, and color variations that are prepared in advance, allowing the recognition algorithm to work with pre-organized data rather than requiring large-scale database acquisition during deployment.
Solution Approach 2:
The invention extracts key features from road signs such as color information, shape characteristics, and pictogram patterns, and stores only these essential features in the database rather than complete images. This extraction approach significantly reduces database size while maintaining recognition accuracy.
2Ease of operation
If color segmentation is used to isolate objects from background, then object isolation is improved, but reliability decreases due to light intensity and fading variations
Solution Approach 1:
The system applies different processing strategies to different parts of the image based on local characteristics. For road signs, it uses color information combined with shape and position constraints, while for other objects it applies different segmentation criteria. This localized approach improves reliability by adapting to specific regional characteristics rather than using uniform color segmentation.
Solution Approach 2:
The invention changes the parameter space by not relying solely on color information but combining it with geometric parameters (shape, size, orientation) and position parameters. This multi-parameter approach compensates for color variations due to lighting and fading, maintaining segmentation reliability under varying environmental conditions.
3Device complexity
If single camera systems are used for road sign detection, then device complexity is reduced, but measurement precision deteriorates due to lack of depth information
Solution Approach 1:
The system transitions from 2D image analysis to 3D spatial understanding by incorporating depth information through multiple cameras. The stereo vision setup provides parallax-based depth measurement, enabling the system to determine object distance, size, and orientation more accurately without significantly increasing overall system complexity.
4Productivity
If real-time processing is implemented for road sign recognition, then productivity is improved, but measurement precision may deteriorate due to processing time constraints
Solution Approach 1:
The recognition system is divided into multiple independent modules: object detection, feature extraction, template matching, and verification. Each module processes specific aspects of recognition independently and in parallel where possible, reducing overall processing time while maintaining accuracy through systematic verification steps.
Solution Approach 2:
The system implements a multi-stage recognition process where not all processing steps are applied to every object. High-confidence detections from preliminary stages may skip verification steps, while ambiguous cases receive full processing. This selective application of processing intensity maintains real-time performance while ensuring accuracy for critical cases.
Data Source
AI summary
An object recognition system is provided including at least one image capturing device configured to capture at least one image, wherein the image includes a plurality of pixels and is represented in an image data set, an object detection device configured to identify a plurality of pixels corresponding to objects from the at least one image, wherein an object includes a plurality of pixels and is represented in an object data set, wherein the object data set includes a set of features corresponding to each pixel in the object, and an image recognition device configured to recognize objects of interest present in an object by image correlation against a set of template images to recognize an object as one of the templates.