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Tensorflow on edge, or – Building a “smart” security camera with a Raspberry Pi

chollinger.com

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Re: Tensorflow on edge, or – Building a “smart” security camera with a Raspberry Pi

#2
Nice writeup but the Raspberry Pi isn't running tensorflow. It is mentioned in the article that the author is sending images to an edge machine.

The big question I had was about hardware video encoding/decoding ... doesn't really cover that. I've found sending single image frames over zeromq to be fairly limiting if you care about high frame rate/low latency processing.

Key issue I have run into is while many chips support hardware video encoding/decoding, the APIs to interface with this aren't there or not in open source. Anyone who has ideas on this, I'd welcome your comment.

As an aside, another option is to run Intel's Movidus USB stick (aka Neural compute stick) and then you get a smart camera on the raspberry pi itself. That raises other issues though.

Re: Tensorflow on edge, or – Building a “smart” security camera with a Raspberry Pi

#4

Nice writeup but the Raspberry Pi isn't running tensorflow. It is mentioned in the article that the author is sending images to an edge machine. The big question I had was about hardware video encoding/decoding ... doesn't really cover that. I've found sending single image frames over zeromq to be fairly limiting if you care about high frame rate/low latency processing. Key issue I have run into is while many chips s…

Shameless plug, check out DOODS: https://github.com/snowzach/doods It's a simple REST/gRPC API for doing object detection with Tensorflow or Tensorflow Lite. It will run on a Raspberry Pi. It actually did support the EdgeTPU hardware accelerator to make the Pi pretty quick for certain models. They broke something so I need to fix EdgeTPU support but it's still usable on the Pi withe the mobilenet models or inception if you're not in a hurry.

Re: Tensorflow on edge, or – Building a “smart” security camera with a Raspberry Pi

#5

Nice writeup but the Raspberry Pi isn't running tensorflow. It is mentioned in the article that the author is sending images to an edge machine. The big question I had was about hardware video encoding/decoding ... doesn't really cover that. I've found sending single image frames over zeromq to be fairly limiting if you care about high frame rate/low latency processing. Key issue I have run into is while many chips s…

Curious what you found limiting about zeromq? Just not enough throughput for high FPS?

So far I’ve found it to be the sanest multicast solution since clients pull.

Re: Tensorflow on edge, or – Building a “smart” security camera with a Raspberry Pi

#7
post #5

Nice writeup but the Raspberry Pi isn't running tensorflow. It is mentioned in the article that the author is sending images to an edge machine. The big question I had was about hardware video encoding/decoding ... doesn't really cover that. I've found sending single image frames over zeromq to be fairly limiting if you care about high frame rate/low latency processing. Key issue I have run into is while many chips s…

Curious what you found limiting about zeromq? Just not enough throughput for high FPS? So far I’ve found it to be the sanest multicast solution since clients pull.

Issue isn't ZeroMQ. The simple/inefficient way to do it is to capture frames one at a time, and send them via ZeroMQ. Video is pretty bandwidth intensive .. the only reason things like YouTube work as smooth as they do is that they use codecs such H264/265 (which are proprietary unfortuantely) and stream compress frames over the network. Now doing the codec in software burns a lot of CPU as this is very math intensive .. most processors support hardware video codecs for this purpose. There are just no open source tools/libraries that make this good/simple enough that I have found.

Re: Tensorflow on edge, or – Building a “smart” security camera with a Raspberry Pi

#8

Is it really edge computing if the pi isn’t running tensorflow? I know the definition is kind of woolly. I wonder what the performance would be on a $100 jetson nano.

Jetson nano can run Cuda code. It is pretty decent.

The article does employ a reasonable definition of edge computing IMO (scientist who works in this area). The rpi is the client, and the processing happens on a beefy edge node. But yeah .. there is not one clear, accepted definition here.

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