Camera Field of Interest Boundary Setting via Local Token Calibration
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Solution Overview
Problem
Existing camera systems face challenges in setting and calibrating a field of interest without knowing the camera's orientation, and there is a need for efficient commissioning methods that do not require image transmission over networks for privacy, security, and bandwidth reasons.
Innovation Solution
A method involving a camera system with a processor and memory that records images of a token in multiple positions to compute a field of interest boundary, allowing for on-device calibration and boundary setting without network coordination, using a transceiver for communication or a user interface for commissioning signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If camera systems transmit images over networks for calibration and boundary setting, then coordination and configuration can be performed remotely, but privacy concerns arise, security risks increase, and bandwidth is consumed
Solution Approach 1:
The patent extracts the calibration and boundary setting process from network-dependent operations to local on-device processing. The camera system performs field of interest boundary computation locally using captured images and token positions, eliminating the need to transmit images over the network while still enabling remote commissioning through wireless communication of only necessary control signals.
Solution Approach 2:
The patent introduces a token as an intermediary object in the calibration process. The token serves as a reference marker that enables the camera system to determine field of interest boundaries without needing to transmit actual images. The token's known positions and characteristics allow local computation of boundaries while maintaining privacy and security.
2Object-affected harmful factors
If camera systems perform on-device calibration and boundary computation, then privacy and security are improved, but the device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-positioning known tokens in the field of interest area before calibration. The token positions and characteristics are predetermined and stored, allowing the camera system to perform boundary computation based on capturing these known references rather than requiring complex image analysis or network coordination during the calibration process.
3Measurement precision
If multiple images are recorded for field of interest boundary computation, then calibration accuracy improves, but the commissioning time increases
Solution Approach 1:
The patent applies self-service by enabling the camera system to automatically capture multiple images at different orientations and autonomously compute the field of interest boundary without requiring manual intervention or image analysis. The system self-calibrates by processing the captured images locally to determine the boundary based on token positions, reducing commissioning time while maintaining accuracy.
Data Source
AI summary
A camera system includes a camera, a processor, and a memory. In response to a first commissioning signal, the camera system records a first image comprising a token in a first position. In response to a second commissioning signal, the camera system records a second image comprising the token in a second position. In response to a third commissioning signal, the camera system records a third image comprising the token in a third position. The camera system computes a field of interest boundary for a visual field of the camera system based on the first position, the second position, and the third position.