Traffic Enforcement Camera Calibration Using Radar Path Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing traffic enforcement devices, particularly mobile ones, face challenges in accurately determining the height and elevation parameters of their cameras due to operational constraints, leading to significant errors in vehicle positioning, which existing methods like FR 3096786 A1 and FR 3131777 A1 are incompatible with.
Innovation Solution
A method for calibrating traffic enforcement devices using a camera and radar to estimate and correct camera parameters, including angle of elevation and height, by aligning camera and radar paths with a road reference frame, and applying interpolation and minimization techniques to achieve accuracy within one tenth of a degree and one tenth of a meter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a total station is used to determine camera position and orientation (as described in FR 3096786 A1), then measurement precision is improved, but device complexity and ease of operation worsen due to incompatibility with mobile traffic enforcement devices that need to be moved to different locations
Solution Approach 1:
The patent replaces the mechanical total station system with an optical-based solution using the camera itself. The camera captures images of the road and sky, and through image processing and geometric calculations, determines position and orientation parameters without requiring external mechanical measurement equipment. This substitution enables mobile operation while maintaining measurement capability.
Solution Approach 2:
The camera system performs self-calibration by using its own captured images of the road and sky to determine its position and orientation. The system processes its own operational data (images) to derive calibration parameters, eliminating the need for separate calibration equipment or procedures that would hinder mobile operation.
2Adaptability or versatility
If the camera is located close to the ground with a grazing orientation for mobile operation, then adaptability is improved, but measurement precision worsens due to substantial effects of small uncertainties in elevation and height parameters on vehicle position determination
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously captures road and sky images, processes them to determine current position and orientation parameters, and uses these parameters to correct and refine vehicle position calculations. This closed-loop approach compensates for the sensitivity issues inherent in grazing orientation by constantly updating and refining the measurement parameters based on actual observed data.
Solution Approach 2:
The patent introduces an additional dimensional element by incorporating sky images alongside road images. By using both vertical (road) and angular (sky) information, the system creates a multi-dimensional calibration approach that provides redundant information and improves the precision of elevation and height parameter determination, thereby compensating for the challenges of grazing orientation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method enables precise calibration of camera parameters, reducing errors in vehicle positioning to achieve accurate speed checks compliant with legislative requirements.
Implementation Method 1
a radar, the method comprising: estimating position and orientation parameters of the camera and radar in a reference frame (O, x, y, z) associated with a lane of a road; acquiring, by means of the camera and radar, data relating to the path of at least one given vehicle
Data Source
AI summary
A method for calibrating a traffic enforcement device comprising a camera and a radar, the method comprising: estimating position and orientation parameters of the camera and the radar in a reference frame associated with a lane of a road; acquiring, data relating to the path of a given vehicle in the lane of the road; determining, a camera path of the vehicle based on the data acquired by the camera and of the estimated position and orientation parameters, and determining, a radar path of the vehicle based on the data acquired by the radar and of the estimated position and orientation parameters; and computing, based on the camera path and of the radar path, corrected values for the estimated position and orientation parameters of the camera, including a parameter quantifying the angle of elevation, and a parameter quantifying the height, of the camera.