Traffic Camera Installation State Estimation Without Manual Calibration

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Solution Overview

Problem

Existing camera calibration systems require a measurement vehicle and manual input of road lanes, which is time-consuming and labor-intensive.

Innovation Solution

A state estimation device utilizing machine learning models to estimate the installation state of imaging devices without manual work or traffic regulation, by analyzing image data from moving objects and road features using first and second state estimators and a feature estimator.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If a measurement vehicle and manual input of road lanes are used for camera calibration, then calibration accuracy can be achieved, but the process becomes time-consuming and labor-intensive

Engineering Contradiction:

Improvecalibration accuracyVSAvoidcalibration time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system uses naturally occurring road markings and moving objects in the traffic environment to perform self-calibration of the imaging device. The calibration process does not require external measurement vehicles or manual intervention - the environment itself provides the reference data needed for calibration through machine learning algorithms that automatically detect and utilize road lanes, vehicles, and other traffic elements.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical calibration system (measurement vehicle with physical markers) with an information-based system using machine learning. Instead of physically transporting a measurement vehicle to multiple locations, the system processes image data from the traffic environment to automatically determine calibration parameters, substituting mechanical procedures with computational algorithms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If a measurement vehicle and manual input are used for calibration, then accurate installation state estimation can be achieved, but operator involvement and traffic regulation are required

Engineering Contradiction:

Improveinstallation state estimation accuracyVSAvoidcalibration operation simplicity

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The imaging device performs automatic calibration using the surrounding traffic environment as reference. The system independently identifies road markings, moving objects, and spatial relationships without human assistance, transforming a previously operator-dependent process into an autonomous self-calibration system that maintains accuracy while eliminating manual involvement.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The calibration system utilizes multiple elements from the traffic environment (road markings, moving vehicles, pedestrians, infrastructure) as diverse reference objects for calibration. This multi-functional approach allows the system to perform accurate calibration using various available environmental features rather than requiring a specialized measurement vehicle, thereby simplifying operations while maintaining precision.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Reliability

If traditional calibration methods are used, then calibration data can be obtained, but dedicated jigs and manual work are required

Engineering Contradiction:

Improvecalibration data reliabilityVSAvoidcalibration system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent extracts calibration information directly from the operational traffic environment rather than requiring separate calibration procedures with dedicated equipment. The system extracts spatial and geometric data from naturally present road markings, vehicles, and infrastructure elements, eliminating the need for specialized calibration jigs and measurement tools while maintaining data reliability through machine learning-based analysis.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The calibration function is merged with the normal operational imaging function. The same imaging device used for traffic monitoring is also used for calibration, and the calibration process is integrated into the regular data collection workflow. This combination eliminates the need for separate calibration equipment and procedures, reducing system complexity while ensuring calibration data reflects actual operational conditions.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS20260004591A1State estimation device, state estimation method, and state estimation program

Publication Date: 2026.01.01 KYOCERA CORP

AI summary

A state estimation device (100) includesa first state estimator for estimating first feature amount data from input image data, a second state estimator for estimating second feature amount data from input image data, a feature estimator for estimating installation state parameters of an imaging device having obtained input image data by imaging from data obtained by combining first feature amount data and second feature amount data with a state estimation model subjected to machine learning so as to estimate installation state parameters of the imaging device having obtained input image data by imaging by using third teacher data including image data obtained by the imaging device having obtained a traffic environment by imaging and correct value data of the installation state parameters of the imaging device having obtained the image data by imaging, and a diagnosis unit for diagnosing an installation state of the imaging device based on the estimated installation state parameter.