Camera Orientation Estimation Using Virtual Cube Vanishing Points
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Solution Overview
Problem
Existing methods for estimating camera orientation relative to a ground surface, particularly in machine vision applications, are cumbersome and error-prone, especially for fleets of vehicles, as they rely on manual calibration and vertical vanishing points, which are not always available or stable over time.
Innovation Solution
A method that involves capturing images, detecting line segments, classifying them into orthogonal directions using virtual cubes with rotating vanishing points, and iteratively computing the optimal orientation of these cubes to estimate the ground plane, allowing for automated camera orientation estimation without relying on vertical vanishing points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration procedure is used to determine camera orientation, then camera orientation can be made known initially, but it requires repetitive calibration for every vehicle and calibration drifts over time
Solution Approach 1:
The system performs self-calibration by automatically computing camera orientation from images captured during normal operation. The onboard processor analyzes line segments in the image and computes vanishing points to determine camera pose relative to the ground plane, eliminating the need for manual calibration procedures.
Solution Approach 2:
The system transitions from fixed manual calibration parameters to dynamic parameters that are continuously updated based on image analysis. By computing vanishing points from line segments in real-time images, the system adapts camera orientation parameters to compensate for drift caused by vibrations, temperature changes, and mechanical wear.
2Extent of automation
If vertical vanishing points are used to estimate ground plane, then camera orientation can be estimated from a single image, but it is impossible when no vertical structure is visible in the captured image
Solution Approach 1:
Instead of requiring vertical structures to compute vertical vanishing points, the system inverts the approach by computing the ground plane first from non-vertical line segments, then using the ground plane to determine camera orientation. This allows estimation even when vertical structures are absent.
Solution Approach 2:
The system segments line segments in the image by their spatial and directional properties, grouping them into categories such as ground-aligned, vertical, or arbitrary orientations. By analyzing different segments differently, the system can compute vanishing points and ground plane even when vertical segments are missing.
3Reliability
If repetitive manual calibration is performed on every vehicle in a fleet, then each vehicle has known camera orientation, but the process is troublesome and error-prone
Solution Approach 1:
Each vehicle in the fleet performs self-calibration independently using its onboard camera and processor. The system automatically computes camera orientation from images captured during normal operation, eliminating the need for centralized manual calibration procedures and reducing deployment complexity.
Solution Approach 2:
The calibration system is designed to be universally applicable across all vehicles in the fleet using the same image processing algorithm. The system handles various scene types (with or without vertical structures, different ground textures) through robust line segment analysis, making it suitable for deploying across diverse vehicle types and operating conditions.
Data Source
AI summary
A method for estimating camera orientation relative to a ground surface. Line segments are detected from an image captured by a camera. A first virtual cube having three orthogonal vanishing points with a random 3D orientation is superimposed on to the image. The line segments of the image are classified grouped into 3D-directional groups. A second virtual cube is superimposed on to the image with an initial 3D orientation. An optimal 3D orientation of the second virtual cube is computed by iteratively changing the 3D orientation of the second virtual cube and measuring perpendicular distances of the three orthogonal vanishing points to the three line segment groups in each iteration starting with the initial 3D orientation, wherein the optimal 3D orientation of the second virtual cube being one that provides shortest perpendicular distances. Co-variances of the orthogonal vanishing points of the second virtual cube at the optimal orientation are computed. Ground orientation is computed from the second virtual cube at the optimal orientation.