Road Region Detection Using Top View Movement Vectors
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Solution Overview
Problem
Current image-based road detection methods, such as those using CRF and MRF models, suffer from high error rates in identifying road regions, necessitating a more robust approach.
Innovation Solution
A method involving the conversion of images into top views and the use of a movement vector matrix to determine whether a candidate point belongs to the road region by analyzing its position change between time points, with the matrix calculated based on camera movement and extrinsic parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CRF or MRF models are used for pixel-scale road region identification, then detailed road detection is achieved, but error rate becomes unacceptably high
Solution Approach 1:
The patent transforms the problem from 2D image plane analysis to 3D spatial coordinate analysis by converting image coordinates to real-world coordinates using camera extrinsic parameters. This dimensional transformation enables the use of movement vector matrices that operate in a different coordinate space, fundamentally changing how road regions are detected and reducing errors inherent in pure pixel-scale analysis
Solution Approach 2:
The patent changes the parameter space from image pixel coordinates to real-world physical coordinates. By using camera extrinsic parameters (rotation matrix R and translation matrix T) to transform coordinates, the system operates in a parameter space that directly reflects physical reality, improving detection reliability while maintaining precision
2Measurement precision
If movement vector matrix calculation based on camera extrinsic parameters is implemented, then road region detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary calculation of the movement vector matrix using pre-obtained camera extrinsic parameters (rotation matrix R and translation matrix T) from previous frame processing. This preliminary action avoids recalculating these parameters from scratch in each frame, reducing computational complexity while maintaining high detection accuracy through consistent coordinate transformation
Data Source
AI summary
A road region detection method is provided. The method includes: obtaining a first image captured by a camera at a first time point and a second image captured by the camera at a second time point (S101), converting the first and second images into a first top view and a second top view, respectively (S103), obtaining a movement vector matrix which substantially represents movement of a road region relative to the camera between the first and second time points (S105), and determining whether a candidate point belongs to the road region by determining whether a position change of the candidate point between the first and second top views conforms to the movement vector matrix. The accuracy and efficiency may be improved.