Automated Camera Configuration via Estimator Models
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Solution Overview
Problem
Deploying and configuring a large number of camera devices in smart city systems is a time-consuming and inefficient process, requiring manual determination of configuration parameters, which wastes computing, networking, human, and transportation resources due to the need for manual measurements and corrections.
Innovation Solution
A registration platform that utilizes modeling to automatically determine configuration parameters for camera devices by processing initial registration data, location data, and map data, employing estimator and parameter optimization models to generate and apply camera and event parameters for optimal configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual determination of configuration parameters is used for camera devices, then configuration accuracy can be achieved through technician expertise, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system enables self-service automation where the camera device automatically determines its own configuration parameters through onboard sensors and processors. The device performs self-calibration by capturing images of calibration patterns and computing intrinsic parameters without human intervention, eliminating the need for manual technician configuration while maintaining accuracy.
Solution Approach 2:
The patent replaces manual mechanical measurement processes with automated computational methods. Instead of technicians physically measuring and configuring cameras, the system uses image processing algorithms and computer vision techniques to automatically calculate configuration parameters from captured images, substituting mechanical human operations with computational automation.
2Adaptability or versatility
If manual configuration processes are used for deploying camera devices, then flexibility in handling diverse scenarios can be maintained, but computing, networking, human, and transportation resources are wasted
Solution Approach 1:
The camera device performs self-configuration and self-calibration autonomously using onboard computational resources. This eliminates the need for external human resources, transportation for technician deployment, and manual intervention, while the adaptive algorithms maintain versatility across different deployment scenarios through automated parameter adjustment.
Solution Approach 2:
The system automatically adjusts camera configuration parameters based on environmental conditions and deployment requirements. By dynamically changing parameters such as exposure, gain, and calibration values based on real-time sensor data and image analysis, the system adapts to diverse scenarios without requiring manual reconfiguration or additional resources.
3Productivity
If automated modeling approaches are used to determine configuration parameters, then time and resource efficiency are improved, but system complexity increases
Solution Approach 1:
The camera device incorporates integrated onboard processing capabilities that perform automated calibration and configuration tasks using built-in sensors and computational algorithms. This self-service approach eliminates the need for external complex configuration systems while maintaining high productivity through automated parameter determination.
Solution Approach 2:
The system uses digital copies and representations of calibration patterns captured by the camera to compute configuration parameters. By processing image data and creating computational models from captured scenes rather than requiring complex physical measurement equipment, the system achieves automated configuration with reduced overall system complexity.
Data Source
AI summary
A device may receive initial registration data identifying initial camera parameters associated with a camera device provided at a location, and may receive location data associated with the location captured by the camera device. The device may receive map data identifying a map image of the location, and may transform the initial registration data into estimated camera parameters. The device may process the location data, with the estimator model, to generate extracted data, and may process the estimated camera parameters, the extracted data, and the map data, with a parameter optimization model, to identify camera parameters for the camera device. The device may provide the camera parameters to the camera device to cause the camera device to be configured based on the camera parameters.