Camera Event Recognition for Equipment Object Control
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Solution Overview
Problem
Existing systems lack efficient methods for recognizing events involving equipment objects and controlling them based on image data captured by cameras, particularly in complex environments with varying conditions.
Innovation Solution
An event recognition system comprising a camera and a controller that processes image data to recognize events, execute procedures, and transmit data to network-based clients, utilizing algorithms to determine parameters and compare them with reference data for controlling equipment objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data is captured and processed to recognize events involving equipment objects, then event recognition capability is improved, but system complexity increases
Solution Approach 1:
The system segments the complex event recognition task into distinct modules: image capture by camera, parameter determination from image data, parameter comparison with reference data, and event recognition based on deviations. This modular segmentation allows each component to focus on a specific function, improving recognition capability while managing system complexity through organized functional divisions.
Solution Approach 2:
The controller acts as an intermediary between the camera system and the equipment object. It receives image data, processes it through parameter determination and comparison, and then triggers equipment procedures based on recognized events. This intermediary role simplifies the overall system architecture by centralizing the complex processing logic in the controller, separating it from both the capture and execution components.
2Productivity
If real-time event recognition and control is implemented, then operational efficiency is improved, but processing time requirements increase
Solution Approach 1:
The system performs preliminary actions by continuously capturing image data and maintaining reference parameter data in advance of actual event detection. The controller is pre-configured with reference data and ready-to-execute equipment procedures. When events occur, the system compares current parameters against pre-stored reference data and can immediately trigger pre-programmed responses, reducing real-time processing delays while maintaining high operational efficiency.
3Measurement precision
If multiple parameters are determined and compared for event recognition, then recognition accuracy is improved, but computational complexity increases
Solution Approach 1:
The system applies local quality by focusing computational resources on specific critical parameters relevant to particular equipment objects and event types. Rather than uniformly processing all possible parameters, the controller determines and compares only the locally relevant parameters needed for accurate event recognition in each specific context, thereby maintaining high recognition accuracy while reducing unnecessary computational complexity.
Data Source
AI summary
An event recognition system includes one or more processing circuits including one or more memory devices and one or more processors. The one or more memory devices are configured to store instructions thereon that, when executed by the one or more processors, cause the one or more processors to: execute an algorithm that receives image data as an input and recognizes a first equipment object and a second equipment object in the image data; recognize an event involving the first equipment object and the second equipment object based upon the image data containing the recognized first equipment object and the recognized second equipment object; and execute an event procedure based upon the event where the event procedure includes controlling the first equipment object.