Adaptive Corresponding Point Search for In-Vehicle Cameras
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Solution Overview
Problem
Conventional corresponding point searching methods for images captured by in-vehicle cameras often result in incorrect matches due to unpredictable search accuracy, especially when dealing with diverse image types.
Innovation Solution
A multi-step corresponding point searching method that employs both the Viterbi algorithm and optical flow to find corresponding points between images, with a search accuracy judgment step to select the most accurate method based on reference values, ensuring high accuracy by switching between methods when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single predetermined searching method is used for all image types, then the device complexity is reduced, but the search accuracy becomes unpredictable and often incorrect
Solution Approach 1:
The patent applies dynamics by making the searching method selection adaptive rather than fixed. The system dynamically switches between the first searching method (Viterbi algorithm) and the second searching method (optical flow) based on the determined search accuracy. When accuracy falls below the reference value, the system transitions to the alternative method, creating a flexible, condition-responsive searching system that optimizes accuracy across diverse image types.
Solution Approach 2:
The patent changes the parameter of searching method selection based on search accuracy evaluation. By monitoring the search accuracy parameter and comparing it against a reference value, the system adjusts which searching method is employed. This parameter-driven approach allows the system to adapt to different image characteristics and maintain high accuracy without requiring a completely complex multi-method framework for all cases.
2Measurement precision
If multiple searching methods are always used, then the search accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The patent implements partial action by selectively applying the second searching method only when necessary. The system first attempts the efficient first searching method (Viterbi algorithm) and only invokes the second method (optical flow) when the accuracy of the first method falls below the reference value. This partial application of the more computationally intensive method minimizes processing time while ensuring accuracy is maintained when needed.
Solution Approach 2:
The system employs feedback by evaluating the search accuracy obtained from the first searching method and using this evaluation to determine whether to apply the second searching method. The feedback loop compares the achieved accuracy against the reference value and adjusts the searching strategy accordingly, preventing unnecessary computational overhead when the first method already provides sufficient accuracy.
3Productivity
If the search accuracy threshold is set low, then more corresponding points are accepted, but the reliability of distance detection decreases
Solution Approach 1:
The patent applies preliminary action by performing accuracy determination and method selection before final corresponding point selection. The system evaluates search accuracy and selects the appropriate searching method in advance, ensuring that only high-quality corresponding points are ultimately selected. This preliminary filtering and method selection process prevents low-accuracy points from being accepted, thereby maintaining distance detection reliability while still achieving high productivity through efficient processing.
Data Source
AI summary
A corresponding point searching method searches corresponding points in plural images, acquired by in-vehicle cameras, for each pixel in a reference image by using a predetermined first method, for example, the Viterbi algorithm. The method searches corresponding points in the plural images for each pixel in the reference image by using a predetermined second method, for example, an optical flow method. The method detects whether or not a search accuracy of the corresponding points in each region divided in the reference image obtained by the predetermined first method is not less than a reference value. When not less than the reference value, the method selects the corresponding points obtained by the predetermined first method. When less than the reference value, the searching method selects the corresponding points obtained by the predetermined second method. The searching method provides the corresponding points between the plural images with a high accuracy.