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From camera to workflow

A single detection starts as pixels in a camera frame and ends as structured data in your n8n workflow. Here’s what happens at each stage.

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1. Camera detection

The Worlds platform processes camera feeds using computer vision models. Each frame produces zero or more detections — identified objects with:
  • Bounding box — pixel coordinates of the object in the frame
  • Object type (tag) — what was detected (person, forklift, AMR, etc.)
  • Confidence score — how certain the model is
  • Timestamp — when the frame was captured
  • Geo-coordinates — if the camera is geo-calibrated

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2. Detection stream

Detections are published in real time via the Worlds GraphQL API over WebSocket. A busy site with many cameras can produce hundreds of detections per second.

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3. State machine processing

The state machine subscribes to the detection stream and does the heavy lifting:
  • Track correlation — groups sequential detections of the same object into a track
  • State enrichment — calculates velocity (rolling average and instantaneous), zone intersections, dwell times, and motion history
  • Zone tracking — maintains active zones (where the track currently is), zone history (where it was), and zone sequence (order of zones visited)
  • Signal generation — emits signals to your workflow based on the subscription’s signal type
The state machine operates in two modes:
  • Streaming — emits signals in real time as detections occur. Supports both track state signals (track_created, track_updated, track_expired) and zone state signals (zone_occupied, zone_updated, zone_empty).
  • Batch — collects detections and emits track summaries at a configurable interval. Only emits expired tracks, but includes interaction data — proximity and overlap between tracks calculated by interpolating bounding boxes across a 1-second window around each detection.
Each signal includes the complete enriched state — your workflow never needs to query for additional data.

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4. Webhook delivery

The state machine delivers signals to n8n via HTTP webhooks. Each workflow registers a webhook URL through its trigger node. The state machine:
  • Matches each detection against registered subscriptions (by data source, object type, etc.)
  • Delivers matching signals to the appropriate webhook URLs
  • Processes detections sequentially per data source to prevent race conditions

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5. Workflow execution

Your n8n workflow receives the enriched state and runs your business logic. A single workflow execution handles one signal — either one track state update or one zone state update. The purpose of the workflow is to distill high-volume detection data into actionable events on the Worlds platform. The typical workflow pattern is: Trigger → Check → Event Orchestrator → Action (image capture, event creation, email alert).

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Data flow example

Here’s a concrete example of a forklift being tracked: 
Frame 1: Forklift detected at (500, 300) in camera "Loading Dock A"
Frame 2: Same forklift at (502, 301) — correlated to same track
Frame 3: Forklift at (503, 301) — enters Zone "Loading Bay 1"
...
Frame 900: Forklift still at (505, 302) — dwell time now 300s, velocity ~0.1 px/s
By frame 900, the state machine has built a rich track state object: 
{
  "signal": "track_updated",
  "track_state": {
    "track_id": "019bb7ea-4050-...",
    "tag": "forklift",
    "datasource_name": "Loading Dock A",
    "motion": {
      "pix": { "velocity": 0.1, "distance": 12.5 }
    },
    "zones": {
      "active": {
        "42": {
          "zone_name": "Loading Bay 1",
          "dwell_time": 300,
          "intersection": { "current_percent": 85.2 }
        }
      }
    }
  }
}
Your workflow receives this and can apply a Type I check: “Is this forklift in Loading Bay 1, with dwell > 300s, velocity < 2 px/s?” If yes, create an event.