No need to tag every person or sticker every asset. Camera video analysis and real-time object recognition, alone can capture safety, foot-traffic, and equipment data.
Camera Recognition & Event Processing Architecture
The core technology behind this: AI analyzes camera footage in real time, recognizing people and objects and generating site events.
※ Actual recognition accuracy and response time may vary depending on lighting, install angle, resolution, and other shooting conditions.
The GrowSpace engine's vision AI detects people, hard hats, vehicles, and other objects in real time.
※ A confidence threshold is applied to reduce false detections at screen edges and overlapping regions.
※ When operating multiple cameras, per-camera recognition zones are separated to minimize double-counting.
Determines detected objects' attributes and state in real time. Analyzes hard-hat compliance, headcount, and direction of movement, so the site gateway can trigger control signals immediately.
To reduce false alarms (false positives) from duplicate detection, object-tracking (tracking) filtering is applied.
※ At the same time, operational settings such as minimum recognition frame count and re-detection delay can be configured to reduce missed detections (false negatives).
※ Considerations for Deployment
Camera vision events go beyond simple alerts — they connect seamlessly with on-site equipment control and data systems.
Reads on-site equipment signals (run state, counts, temperature, etc.) from PLCs alongside camera analysis results and sends them to the server. Supports industry-standard interfaces such as Modbus TCP and Digital I/O.
Streams recognition events and statistics in real time to existing management systems such as ERP, MES, WMS, and control dashboards. Data exchange is straightforward via standardized protocols.
Tag-Based Recognition vs. Camera Vision Technology
The right recognition method should be chosen based on site requirements and installation conditions.
| Spec | Camera Vision (Video Recognition) | UWB/RFID (Tag-Based) | Manual Check (Human) |
|---|---|---|---|
| Detection Accuracy | Position + state recognition possible (video-based judgment of compliance, direction, etc.) | Focused on position/pass-through (limited state recognition) | Depends on human observation (misses and errors possible) |
| Response Speed | Immediate (real-time) | Immediate (at the moment of crossing) | Slow (requires a person to be present) |
| Indoor Use | Possible (with adequate lighting) | Possible | Possible |
| Deployment Complexity | Low (existing CCTV infrastructure can be used) | Low–High (varies by method, requires tag/reader installation) | Low (no extra equipment needed, but requires personnel) |
By integrating with warning lights, sirens, automatic doors, and ERP systems,
the system responds to hazardous situations before a person has to step in.
It goes beyond simple video capture — automatically triggering events suited to the situation.
Cameras installed on-site capture video in real time.
AI recognizes people, objects, and equipment status in the video in real time.
Status is determined according to pre-configured rules such as safety regulations and counting criteria.
Follow-up actions are carried out, including sending alerts, logging data, and equipment integration.
Cameras throughout manufacturing and construction sites detect hard-hat compliance in real time. When non-compliance is detected, an alert is sent to the manager immediately to help prevent accidents.
Cameras at entrances and key pathways automatically count the number of people passing through. Time-based traffic patterns can be used for staffing decisions and congestion management.
Camera vision analytics data and equipment signals such as PLC data are sent to the server together, so you can view on-site safety and equipment status in one dashboard.
GrowSpace Camera Vision builds a smart monitoring system using your existing camera infrastructure.
Tell us which zones need which alerts, and we’ll review the zone design and integration approach first.
