Self-Service Camera Calibration via 3D Scanning and Cloud Feedback
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Solution Overview
Problem
Existing monitoring systems for real spaces face challenges in efficiently setting up and recalibrating cameras, especially in dynamic environments like retail stores, due to variability in camera placement and drift over time.
Innovation Solution
A self-service system that includes scanning the area to generate a 3D representation, placing cameras, configuring computing devices to connect with image processing services, and calibrating cameras using cloud-based applications, enabling minimal human intervention and automatic recalibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual setup and calibration of cameras is performed, then initial configuration can be completed, but considerable time and effort are required
Solution Approach 1:
The system enables self-service calibration where the monitoring system automatically calibrates cameras using detected objects and features in the environment. The camera calibration engine performs autonomous calibration without requiring manual intervention, allowing the system to self-configure and adapt to environmental changes over time.
Solution Approach 2:
The system performs preliminary calibration actions by detecting objects and features in advance to establish initial camera parameters. By pre-processing environmental data and object detections, the system prepares calibration information before formal camera setup, reducing the time required for initial configuration.
2Reliability
If cameras are recalibrated frequently to account for drift and environmental changes, then monitoring accuracy is maintained, but operations may be impacted
Solution Approach 1:
The system implements periodic calibration at predetermined intervals rather than continuous calibration. The camera calibration engine automatically recalibrates cameras at scheduled times based on drift detection thresholds, maintaining monitoring accuracy while minimizing disruptions to operations through time-based periodic maintenance.
Solution Approach 2:
The system uses feedback from drift detection mechanisms to trigger calibration only when necessary. By continuously monitoring camera performance and detecting drift beyond acceptable thresholds, the system activates calibration selectively, maintaining reliability while avoiding unnecessary recalibration that would impact productivity.
3Extent of automation
If automated self-service setup is implemented, then minimal human intervention is required, but system complexity increases
Solution Approach 1:
The system employs a universal camera calibration engine that handles multiple camera types, configurations, and calibration scenarios through a single automated platform. This multi-functional engine can perform initial setup, periodic recalibration, and drift correction across diverse monitoring environments, reducing the need for specialized procedures for each situation.
Solution Approach 2:
The system introduces a camera calibration engine as an intermediary component that mediates between raw camera data and processed monitoring outputs. This intermediate layer automatically handles complex calibration computations and parameter adjustments, shielding users from the underlying complexity while enabling high-level automation.
Data Source
AI summary
The technology disclosed teaches systems and methods for self-service installation of monitoring operations in an area of real space, the method including scanning the area of real space to generate a 3D representation of the area of real space, placing a camera at an initial location and orientation for monitoring a zone within the area of real space, configuring a computing device to be connected to a cloud network hosting an image processing service and couplable to the camera, coupling the camera to the computing device via a local connection using a unique identifier associated with the camera, and finetuning the camera placement to a calibrated location and orientation, wherein the finetuning is assisted by information received from a cloud-based application associated with the image processing service.