Template-Based Event Recognition for Equipment Interaction Control
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Solution Overview
Problem
Existing systems lack an efficient method to recognize events involving equipment objects in real-time using camera systems, leading to inadequate control and monitoring capabilities, particularly in dynamic environments like airports and industrial settings.
Innovation Solution
An event recognition system comprising a camera and a controller that captures image data, processes it to recognize events, and executes procedures such as controlling equipment objects, transmitting data to network-based clients, and managing image data communication through a video frame transmission service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time event recognition is implemented using camera systems, then operational efficiency and safety are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments event recognition into multiple specialized modules: image data capture module, event recognition module (with template matching and parameter comparison), and control module. This segmentation allows each module to focus on specific tasks, improving overall efficiency while managing complexity through modular design.
Solution Approach 2:
The system performs preliminary actions by pre-storing equipment object templates and parameters in the controller before runtime. During operation, the system directly compares captured image data against these pre-prepared templates, eliminating the need for complex real-time analysis and reducing computational complexity while maintaining high recognition speed.
2Measurement precision
If comprehensive image data processing is performed to recognize equipment objects and events, then measurement precision is improved, but processing time and computational load increase
Solution Approach 1:
The system replaces complex mechanical image analysis with optical pattern matching techniques. The controller uses template matching algorithms that compare image data against pre-stored equipment object templates, achieving high recognition accuracy through mathematical operations rather than intensive computational processing, thus reducing processing time.
Solution Approach 2:
The system changes parameters by extracting key features from image data (such as shape, size, position) and comparing them against stored template parameters. This parameter-based approach transforms complex image recognition into straightforward parameter comparison, maintaining high precision while minimizing processing time.
3Reliability
If multiple cameras are deployed to capture different perspectives, then event detection capability is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The controller is designed with universal functionality to handle image data from multiple cameras simultaneously. It can process images from different perspectives, perform template matching on each, and integrate results to achieve comprehensive event detection. This multi-functional design allows the system to scale from single-camera to multi-camera configurations without requiring fundamentally different architecture.
Data Source
AI summary
Event recognition methods include executing, with a controller, an algorithm which receives image data as an input and recognizes a first equipment object and a second equipment object in the image data; recognizing an event involving the first equipment object and the second equipment object based upon the image data using the controller; executing an event procedure based upon the event, wherein the event procedure includes controlling the first equipment object using the controller; and transmitting the image data to a network-based client based upon the event.