> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/guides/run-a-model-on-a-video.md).

# Run a Model on a Video

A [Workflow](/workflows/what-is-workflows.md) is a multi-step computer vision application you build in your browser by connecting blocks: a model, a tracker, visualizations, and logic. You build it once and run it on images, video files, or live RTSP streams.

On video, a Workflow runs as a pipeline built for real-time speed. [Roboflow Inference](https://inference.roboflow.com/) decodes frames, runs the model, and draws the results in parallel so the GPU stays busy. On a live stream, it skips frames it cannot keep up with so it stays on the latest frame instead of falling behind.

This guide builds a Workflow that detects vehicles, tracks each one across frames, and counts them as they cross a line, then runs it on a sample video.

## Build and run the Workflow

{% stepper %}
{% step %}

### Open the Workflow editor

Create a [Roboflow account](https://app.roboflow.com) if you don't have one, then open [app.roboflow.com/workflows](https://app.roboflow.com/workflows) and click "Create Workflow" to open a blank Workflow in the editor.
{% endstep %}

{% step %}

### Add and connect the blocks

Add these blocks and connect them top to bottom, so each block's output feeds the next:

<figure><img src="/files/VEcGluQpxgIugjngynjw" alt="A Workflow connecting an object detection model, tracker, line counter, and visualization blocks"><figcaption><p>The detect, track, and count Workflow</p></figcaption></figure>

* Object Detection Model: runs an [RF-DETR](/deploy/supported-models/rf-detr.md) model on each frame and returns bounding boxes.
* ByteTrack Tracker: assigns a persistent ID to each detection so the same object is followed across frames.
* Line Counter: counts tracked objects as they cross a line you position on the frame.
* Bounding Box Visualization: draws the detection boxes.
* Line Counter Visualization: draws the counting line and the running tally.
* Trace Visualization: draws the path each tracked object has traveled.

To skip building it by hand, open the JSON editor with the `</>` icon at the top left of the editor and paste in [this Workflow definition](https://media.roboflow.com/quickstart/workflow.json).

For a deeper walkthrough of adding and configuring blocks, see [Build a Workflow](/workflows/build-a-workflow.md).
{% endstep %}

{% step %}

### Upload a video and run

Click "Run" (`▶` icon) to open the run panel. Under "Media", select the "File" tab and upload [this sample video](https://media.roboflow.com/quickstart/cars.mp4), then click "Run".

The Workflow processes the video and returns the annotated video with boxes, traces, and a live count.
{% endstep %}
{% endstepper %}

{% embed url="<https://media.roboflow.com/quickstart/tracking-workflow.mp4>" %}

## Where the Workflow runs

By default, "Run" executes the Workflow on the [Serverless Hosted API](/deploy/serverless-hosted-api-v2.md): auto-scaling GPUs in Roboflow's cloud, with no hardware to provision. You can also run the same Workflow on [your own hardware](/guides/run-a-model-locally.md), from a laptop to an NVIDIA Jetson.

* **Serverless**: No hardware to manage and it scales automatically. Each frame makes a network round trip to the cloud, which adds latency, so it suits getting started, images, and batch jobs more than low-latency live video.
* **Local**: Frames never leave the machine, so there is no network hop and per-frame latency is lower, which matters for live streams. In exchange you provision and maintain the hardware yourself.

To lower latency either way, run on a GPU, choose a smaller model (ex: `rfdetr-nano` or `rfdetr-small`), or reduce the input resolution. For a private, single-tenant cloud endpoint, see [Dedicated Deployments](/deploy/dedicated-deployments.md).

## Use a different model

The Object Detection Model block runs a ready-made RF-DETR by default, but you can change it. Click the block and set its model to:

* A model you trained in Roboflow. Copy its model ID (formatted as `{project}/{version}`) from your project in the [Roboflow app](https://app.roboflow.com).
* A public model from [Roboflow Universe](/universe/what-is-roboflow-universe.md).
* A different ready-made [pretrained model](https://inference.roboflow.com/quickstart/aliases/) by alias, such as `rfdetr-nano`, `rfdetr-seg-medium`, or `yolo26l-640`.

## Process many videos at once

To run a Workflow across a large set of videos or images offline, use [Batch Processing](/deploy/batch-processing.md). It is a managed service: select your data and your Workflow, and Roboflow processes the batch in the cloud with no code and no local compute.

## Where to go next

* Run the Workflow on your own hardware, including live webcam and RTSP streams. See [Run a Model Locally](/guides/run-a-model-locally.md).
* Add logic, filtering, zones, and notifications. See [Build a Workflow](/workflows/build-a-workflow.md).
* Understand what each Workflow run costs on the [Serverless pricing page](/deploy/serverless-hosted-api-v2/pricing.md#workflow-run).