Turn Your Cameras into AI Safety Monitors: A Helmet Detection Use Case

Security cameras have kept people and places safe for decades. But a camera is only as useful as the person watching it, and no one can watch every feed for an entire shift. AI safety monitoring takes over that job: it watches your live streams, spots violations, and alerts your team, so you can cover far more cameras with far less effort.

Ant Media Server AI Vision brings this capability to live video streams using general-purpose Vision Language Models (VLMs). Instead of building or training a separate AI model for each use case, AI Vision lets you detect a wide range of situations using simple natural-language prompts. From missing safety equipment and fire hazards to restricted-area access, a single tool can support many different monitoring needs.

In this article, we’ll explore how AI Vision works through a practical use case: detecting workers without safety helmets on a construction site and automatically generating alerts when a violation is detected.

Before We Start

To try this yourself, you’ll need Ant Media Server with the AI Vision plugin and access to a Vision Language Model (VLM), either cloud-based or on-premises.

Don’t have these ready? No problem! You can still follow along to see how everything works. And if you’d like to try AI Vision yourself, contact us for a live demo or a ready-to-use trial environment.

For the following steps, we’ll use the AI Vision web interface, which you can access at:

https://{AntMediaServerURL}/{AppName}/aivision.html

This makes it easy to configure and test AI Vision. However, everything we’ll demonstrate can also be done through REST APIs, without using the web interface, making it easy to integrate AI Vision into your own applications and automated workflows.

Step 1: Ingest a Video Stream

AI Vision can analyze any video stream already ingested into Ant Media Server. You can use an IP camera or publish a stream using protocols such as WebRTC, RTMP, or SRT. Check out the Ant Media Server streaming guides for all available options.

For our helmet detection example, we’ll use a security camera monitoring a construction site and connect it to Ant Media Server via RTSP.

It’s simple:

1. Open the Ant Media Server dashboard and select your application.
2. Click New Live Stream → Stream Source.
3. Enter the camera’s RTSP URL and a stream name.
4. Save the stream and wait until its status changes to Broadcasting.

That’s it! Our construction-site camera is now streaming to Ant Media Server and is ready for AI analysis.

For more details, see the IP Camera Streaming Guide.

Step 2: Configure Your AI Vision Model

Now, let’s configure the AI model that will analyze our video stream.

In the Settings panel on the left, enter your Vision Language Model (VLM) details:

• Base URL: The API endpoint of your vision model.
• Token: Your API key, if required.
• Model: The name of the model you want to use.

You can use either a cloud-based or an on-premises VLM. AI Vision supports OpenAI-compatible APIs, so you can choose a hosted service or run a compatible model on your own infrastructure.

For this example, we’re using OpenAI’s gpt-4o-mini model.

Optionally, you can enter an Alert Webhook URL to receive notifications when AI Vision detects a safety violation.

Finally, click Save to apply your settings.

— AI Vision settings

Your AI Vision model is now ready! Next, we’ll tell it what to look for in our construction-site video.

Step 3: Test the Safe Scenario (No Helmet Violation)

Let’s first test AI Vision with a scene where all workers are wearing safety helmets.

Enter the following instructions in the Prompt field:

Is any person in this frame not wearing a safety helmet?
If yes, respond with ALERT:{brief explanation}.
If everyone is wearing a safety helmet, respond with:
No safety helmet violation detected.

This prompt tells AI Vision what to look for and how to respond when a safety violation is detected. The ALERT: keyword is used to identify violations and trigger alerts.

Make sure Continuous is turned off, then click Analyze to check the current video frame.

AI Vision analyzes the image and returns:

No safety helmet violation detected.

Since everyone is wearing a helmet, AI Vision correctly identifies the scene as safe and displays No alert.

— AI Vision no alert

This is exactly what we want! AI Vision recognizes that no safety violation is present, and no webhook alert is sent. The analysis result is still saved in the dashboard history for later review.

Next, let’s see what happens when someone isn’t wearing a helmet.

Step 4: Detect a Worker Without a Helmet

Now, let’s test what happens when a worker without a safety helmet appears in the video.

Using the same prompt, click Analyze again. No changes to the model or configuration are needed.

This time, AI Vision detects the violation and returns:

ALERT: One person is not wearing a safety helmet.

AI Vision recognizes the ALERT: keyword and displays the result as an alert.

Clicking the alert opens Alert Analysis Detail, where you can see the analyzed frame, stream ID, timestamp, and AI response.

— AI Vision with alert

If you configured a webhook in Step 2, AI Vision also sends the alert details to your endpoint as JSON. You can use this information to notify your team, create an incident report, or trigger other automated actions.

That’s it! With just a simple prompt, we’ve detected a safety violation in a live video stream without training a custom AI model.

Step 5: Enable Continuous Monitoring

So far, we’ve analyzed individual video frames by clicking Analyze. But what if we want AI Vision to monitor our cameras automatically?

Simply enable Continuous, set an analysis interval, and click Analyze. For example, setting the period to 10 seconds tells AI Vision to check the video stream every 10 seconds.

That’s it! AI Vision now checks for missing safety helmets automatically, without anyone having to watch the video.

You can also use Apply to: All to monitor all active streams with the same prompt, including new streams that start later.

All active monitoring tasks appear in the Prompt Queries panel, where you can view results, track alerts, and manage your queries. You can also export and import queries to reuse your monitoring configurations.

From one camera to many, AI Vision makes it easy to scale automated AI safety monitoring.

— AI Vision continuous detection

Conclusion: Beyond Helmet Detection with AI Safety Monitoring

As we’ve seen, Ant Media AI Vision makes it easy to turn live video streams into intelligent monitoring systems. With just a simple prompt, we detected missing safety helmets, generated alerts, and enabled continuous monitoring without training a custom AI model.

And helmet detection is just the beginning! The same tool can detect many other situations, from fire and smoke to restricted-area access, workplace hazards, and more.

Here are a few more things AI Vision can do. Same tool, different prompt:

Product Defect Detection

Spot faulty products on the production line.

— Product Defect Detection with AI Vision

Fall Detection

Get an alert when someone falls.

— Fall Detection with AI Vision

Accident Detection

Catch accidents the moment they happen.

— Accident Detection with AI Vision

Fire Detection

Detect fire and smoke before they spread.

— Fire Detection with AI Vision

To learn more about how AI is changing traditional video surveillance, check out our article on AI Video Surveillance.

What would you monitor first? Contact us for a live demo or a ready-to-use trial environment.