Traffic Sign Classification via Gradient Array Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing traffic sign recognition systems face challenges in robustness and efficiency due to complex information on signs and image noise, with increased computational and memory requirements when incorporating learning units, and insufficient robustness with text recognition across varying fonts.
Innovation Solution
A method for classifying objects in images by defining an image area, decomposing it into subareas, calculating aggregated pixel values, and analyzing gradient arrays to identify the object's class, which reduces data complexity while maintaining useful information for classification, and can be implemented in a driver assistance system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If learning units are incorporated into the system to improve traffic sign recognition ability, then recognition reliability is improved, but computational and memory storage requirements significantly increase
Solution Approach 1:
The image area is decomposed into multiple subareas, and each subarea is processed independently to generate aggregated pixel values. This segmentation reduces the overall computational complexity by breaking down the large-scale image processing into smaller, manageable units that can be handled more efficiently.
Solution Approach 2:
The patent extracts only the essential gradient information from the image data by calculating aggregated pixel values and their gradients. This extraction process removes unnecessary detailed information while retaining the critical features needed for classification, thereby reducing memory storage requirements and computational burden.
2Adaptability or versatility
If text recognition methods are used to handle various fonts on traffic signs, then text detection capability is improved, but robustness decreases due to font variations
Solution Approach 1:
The patent transforms the image data into gradient space by calculating aggregated pixel values and their gradients. This parameter transformation makes the representation invariant to font variations, as gradient patterns capture the essential structural information of text and symbols regardless of their specific font appearance, thereby improving robustness.
3Measurement precision
If detailed image data is used for classification, then classification accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The patent extracts gradient information from aggregated pixel values, which captures the essential edge and boundary information needed for classification. This extraction of critical features maintains classification accuracy while discarding redundant detailed information, thereby reducing processing time and computational load.
Solution Approach 2:
By dividing the image into subareas and processing each independently, the patent enables parallel computation and reduces the overall processing time. The segmented approach allows for more efficient memory management and faster computation compared to processing the entire image as a single unit.
Data Source
AI summary
The invention relates to method of classifying an object (10) in an image (9), the method comprising the steps of:defining an image area (11) located within the object (10) in the image (9),decomposing the image area (11) into an array of subareas (13),defining an array of aggregated pixel values by calculating, for each of the subareas (13), an aggregated pixel value of the respective subarea (13),calculating a gradient array depending on differences between the aggregated pixel values of adjacent subareas (13),analyzing the gradient array,identifying, depending on a result of the analyzing step, the object (10) as belonging to a class of a predefined set of classes.Furthermore, the invention relates to a device (4) for analyzing an object (10) contained in an image (9) and to a driver assistance system as well as to a vehicle (1) containing such a device (4).