Raised Object Recognition via Multi-Camera Gradient Analysis
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Solution Overview
Problem
Current camera systems struggle to differentiate between flat objects like road markings and raised objects, such as border stones, in two-dimensional images, making it difficult to recognize raised objects in a motor vehicle's environment, especially during maneuvering or in narrow spaces.
Innovation Solution
The method involves capturing images from two cameras with different perspectives, transforming them into a common reference system, and analyzing gradients to identify maxima that correspond to the boundaries of raised objects by determining deviations in distance, which indicates the presence of a raised object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single camera is used to capture the environment, then the device complexity is low, but the ability to recognize raised objects is insufficient
Solution Approach 1:
The patent combines images from multiple cameras (first camera and second camera) to create a composite view that enables raised object recognition. By merging the perspectives of at least two cameras, the system achieves three-dimensional effect and can differentiate between flat and raised objects, resolving the contradiction between simple device structure and reliable object recognition.
Solution Approach 2:
The patent transitions from two-dimensional single-camera images to a multi-dimensional analysis by combining images from cameras with different perspectives. This dimensional approach creates a three-dimensional effect that allows the system to detect height differences and recognize raised objects, improving reliability without requiring complex specialized sensors.
2Measurement precision
If multiple cameras with different perspectives are used, then raised object recognition is improved, but the device complexity increases
Solution Approach 1:
The patent uses multiple cameras positioned at different perspectives to capture images of the same environmental region. By processing these multi-perspective images and analyzing gradient differences, the system achieves precise raised object detection through three-dimensional effect, improving measurement precision while keeping the camera system relatively simple.
Solution Approach 2:
The patent creates a virtual three-dimensional representation by processing and combining images from multiple cameras. This copying approach allows the system to simulate depth and height information without requiring complex specialized three-dimensional sensors, thereby improving detection accuracy while maintaining manageable device complexity.
3Reliability
If image transformation and gradient analysis are performed, then raised object identification is improved, but the processing complexity increases
Solution Approach 1:
The patent extracts gradient information from transformed images to identify raised objects. By focusing on gradient analysis of specific image features rather than processing entire images, the system achieves reliable raised object recognition while reducing processing complexity. The gradient extraction isolates the critical information needed for detection.
Solution Approach 2:
The patent replaces complex mechanical three-dimensional sensing systems with optical image processing and mathematical gradient analysis. This substitution allows the system to achieve reliable raised object recognition through software-based image transformation and gradient calculation, reducing overall system complexity while maintaining high detection reliability.
Data Source
AI summary
The invention relates to a method for recognizing a raised object on the basis of images in an environmental region of a motor vehicle, comprising: capturing a first image from a first camera and a second image from a second camera, transforming the first and second images into a common reference system, forming gradients over pixel values of pixels along gradient lines in the transformed first and second images, summing gradients along parallel summation lines to form a gradient sum, determining a first pair of the maxima of the gradient sum in the transformed first image and a second pair of the maxima of the gradient sum in the transformed second image, recognizing the raised object, if at least one distance between the maxima of the first pair in the transformed first image deviates from a distance of the maxima of the corresponding second pair in the transformed second image.