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This guide walks through how to go from raw camera feeds to a working vision model and automated workflow. The process is iterative by design, expect to circle back and refine as you learn what your model does and doesn’t catch.

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Before You Start

Tip: Define your use case before you touch anything. Know what events or objects you actually need to detect (a forklift entering a zone, inventory leaving storage, a pallet jack in use, etc). That goal shapes every decision below, from how you group cameras to what you tag. Warning: If you’re new to the platform, build and test in a non-production or demo environment first. Making changes directly in a live/production environment risks breaking real workflows other teams depend on.

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Step 1: Organize Cameras into Collections

A collection is a folder that groups cameras which will share the same vision model. How to decide what goes together:
  • Group cameras that share visual context, similar environments, looking for similar things.
  • Keep visually distinct feeds separate. An exterior camera and an interior camera will generally perform better as separate collections with separate models rather than one shared model.
  • A camera isn’t locked into one collection. The same camera can belong to multiple collections, for example, running a specialized model in one collection while also contributing to a general “people detection” collection.
Example: A two-camera collection covering a loading dock area, where both cameras track people, a forklift, inventory, and a pallet jack, just from different angles. Because both cameras are watching the same kind of activity in the same environment, they share one model.

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Step 2: Configure Zones

Once cameras are organized into a collection, configure zones within each camera view.
  • Zones define specific areas of interest inside the frame (a storage area, a dock door, etc).
  • This is the information that later feeds into the workflow builder’s evaluation logic, it’s how the system knows where to look for an event, not just what to look for.
Tip: Set up zones with your end goal in mind. If you want to know when inventory is picked up and moved, or when a pallet jack leaves its storage spot, that’s exactly the kind of zone to configure here.

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Step 3: Tag and Review Detections

This is where you move into the tagging interface and its companion, the inference viewer.
  • The inference viewer shows your video feed alongside a timeline of every object detected and tracked during that period.
  • Each object type (forklift, inventory, pallet jack, person, etc) is a label you’ve trained into the model, shown as its own track on the timeline.
  • A solid, continuous block on the timeline means the object was tracked without interruption. A broken-up track means detection was lost and picked back up.
Warning: Broken tracks aren’t necessarily a sign of a bad model. Occlusion is a common cause, if a forklift drives in front of a tracked object, the system may drop that detection and pick it up as a new track once it’s visible again. This is expected behavior to watch for, not automatically a flaw to fix.

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Step 4: Train and Iterate the Model

  • Train a model version, then review its performance using the inference viewer.
  • This process is intentionally iterative. Expect to be on v2, v3, and beyond as you refine things, it’s normal, not a sign something went wrong the first time.
Tip: If you notice false positives or missed detections, the fix is often to add a new object class so the model has better context for what it’s looking at, rather than assuming the model itself is broken.

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Step 5: Move to the Workflow Builder

With collections, zones, and a trained model in place, the final step is building automated workflows (in the workflow builder) that act on the zone and tag data established in Steps 1-4. Tip: Look at existing workflows and models your team has already built. They’re a useful reference point for structure and strategy before you build your own from scratch.

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Quick Reference: The Flow

  1. Collections - group cameras by shared visual context and goal
  2. Zones - define the specific areas that matter within each camera
  3. Tagging / Inference Review - label objects, review detection tracks
  4. Model Training - train, evaluate, iterate
  5. Workflow Builder - automate based on zones and tags