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This Is How I’ll Be Using Raspberry Pi’s New AI Camera


Key Takeaways

  • The Raspberry Pi AI Camera features hardware to handle AI processing on-device.
  • It can carry out computer vision tasks like semantic segmentation, pose estimation, and object detection.
  • Machine learning can be integrated into its use cases with ease.



The Raspberry Pi AI Camera brings AI vision to almost any Raspberry Pi model. With its neural processing hardware, the AI camera does all the heavy lifting on the device, which means you can now use your Raspberry Pi for more AI projects than ever before.



AR Streaming

twitch streamer wearing sci-fi ar helmet
Leonardo.AI/MakeUseOf

I’ve always wanted to stream on Twitch for laughs, but I’m camera-shy and a bit of a privacy nut. Sure, I could make a 3D model to replace my physical appearance on-screen, but I don’t want my online avatar to be a werewolf or Sonic the Hedgehog—nothing wrong with that if that’s what you’re into.

So, is there some happy medium between revealing my full identity and creating a VTuber avatar? With the Raspberry Pi AI Camera, augmented reality (AR) streaming might be the answer I’ve been looking for.

The Raspberry Pi AI Camera can perform semantic segmentation, which is a fancy way of saying that it can assign meaning to each pixel of a video stream. Low-latency processing could be powerful enough to overlay AR filters on the fly.


Maybe I’ll use an AR filter to give myself one of those futuristic helmets like the ADVENT officers from XCOM wear. It would cover my eyes and hair but leave the other half of my face and body alone. Then I’ll jazz up the room behind me to make my real bookshelves look like they’re part of the bulkheads of a space cruiser.

The Raspberry Pi AI Camera seems like a great way to demonstrate extended reality applications and their possibilities. Sure, it might be a lot of effort just to troll my one or two viewers, but a man can dream.

DIY Party Photo Booth

young people in a photo booth at wedding with amusing glasses and outfits
Gavin Phillips/DALL-E/MakeUseOf

What’s the ultimate way to liven up a party with friends or Christmas get-togethers? You might have guessed alcohol, but I would say a party photo booth.


But renting a photo booth for small gatherings isn’t very practical. It can get expensive very quickly, not to mention the logistics of getting the equipment in and out. That’s where the Raspberry Pi AI Camera comes in.

Since it doesn’t need a GPU or separate Tensor Processing Unit, the Raspberry Pi AI Camera can do much without taking up much space. It’d be portable enough to prop up a DIY photo booth whenever I wanted.

Here’s all I’d need to set one up:

The Pi AI Camera can analyze pose estimation, meaning it can understand how people are standing or gesturing. It can automatically suggest adding props like lightsabers or animated hearts around couples based on what it detects in the frame.


If someone shows up to our New Year’s Eve party and calls my booth super nerdy, I’ll reply that it beats taking photos like it’s 1999.

Automated Wildlife Album

hummingbird in wildlife photo album
Leonardo.AI/MakeUseOf

Living in the WUI (wildland-urban interface), my partner and I have crossed paths with deer, foxes, and all sorts of beautiful hummingbird species that we can’t name. Instead of using binoculars and vague Google searches to identify them, I think I’ll let the Raspberry Pi AI Camera do the work.

There are already amazing Raspberry Pi AI projects that can be done with a regular Pi, but adding the AI Camera will let me easily run neural network models. This ability to incorporate machine learning—including apps that use TensorFlow or PyTorch—will take projects using a Raspberry Pi and camera to the next level.


It would finally let me indulge the lazy nature photographer in me. Here’s how I’d use AI to compile a local wildlife album:

  1. The Raspberry Pi AI Camera uses object detection to snap photos of the animals, insects, and lizards that pass by.
  2. The AI attempts to identify the species automatically.
  3. The photos and identifying information are logged into a wildlife album.
  4. Machine learning is used to update the album to be more accurate as it’s trained on additional data from the camera over time.

Maybe I’ll finally figure out what’s been destroying our azaleas in the middle of the night. Even voles won’t be safe from the AI revolution.

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