Camera View Blockage Lockout for Networked Machine Security
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Solution Overview
Problem
Security cameras with blocked views due to movable objects can compromise the effectiveness of real-time monitoring and asset protection, as they fail to prevent unauthorized access or actions.
Innovation Solution
Implementing a system that uses video analytics to detect when a defined percentage of the camera's field of view is obstructed by a movable object, triggering a lockout mode for the networked machine to prevent unauthorized access or actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security cameras are installed for real-time monitoring, then security deterrence and monitoring capability are improved, but the monitoring effectiveness deteriorates when the camera view is blocked by movable objects
Solution Approach 1:
The system implements a feedback mechanism where the camera continuously monitors its own field of view, and when obstruction is detected, the system responds by locking out the machine. This closed-loop feedback ensures that the monitoring system actively maintains security effectiveness by detecting view blockages and triggering appropriate responses.
Solution Approach 2:
The camera acts as an intermediary between the physical security environment and the control system. By positioning the camera to monitor both the work area and the machine interface, it serves as a mediator that detects when unauthorized persons are present and triggers the lockout mechanism, thus maintaining security without requiring direct physical intervention.
2Measurement precision
If the camera continuously monitors to detect blocked views, then security detection capability is improved, but energy consumption and system complexity increase
Solution Approach 1:
Instead of continuous analysis, the system uses periodic sampling of video frames at predetermined intervals. This approach maintains adequate detection precision by checking for obstructions at regular intervals while significantly reducing the computational load and energy consumption compared to continuous frame-by-frame analysis.
Solution Approach 2:
The system analyzes only specific portions of the video feed corresponding to the critical field of view areas rather than processing the entire video stream. This partial action approach focuses computational resources on the most security-critical regions, maintaining detection accuracy while reducing overall energy consumption.
Data Source
AI summary
A method and system for a lockout in response to a blocked view is disclosed. The method is carried out within at least one network. The at least one network includes at least one network addressable machine and at least one fixed-location camera having a respective Field Of View (FOV). The method includes creating an operation dependency definition between the fixed-location camera and the network addressable machine based on an inclusion of the network addressable machine within the FOV. Video analytics is employed to generate a blocked view alert in response to a blocked view threshold being satisfied in relation to the live video captured by the fixed-location camera. In response to the blocked view alert, the network addressable machine is caused to enter a lockout mode.