Choosing the correct architecture for 100's of cameras

Accelerated Computing Intelligent Video Analytics DeepStream SDK
1

Please provide complete information as applicable to your setup.

• Hardware Platform (Jetson / GPU)

GPU

• DeepStream Version

6.0

I have an application that will have 100’s of rtsp cameras per zone. Currently we have a test site with 100 cameras in a single zone. Some sites will have multiple zones, say 6 zones and each zone could have 150 cameras.

My application is similar to tracking people with action recognition. Think of something like a mall to track individual people as they walk around and identify what they are doing - walking, eating etc.

I am choosing hardware and I want to make sure I have chosen the correct architecture for this.

My current approach is to use deepstream with an object detector PGIE and then pass on to a action recognition SGIE. Information will be output over kafka to allow a third-party tracker to accumulate the location information from each camera and track movement across cameras.

Inference must be done on site due to bandwidth limitations. I have chosen the A5000 GPU since it seems to give the best performance vs cost. I will need multiple A5000 since one is not enough, perhaps e.g. 10 GPUs depending on framerate.

I’m not sure whether to maximise the number of GPUs in a single server PC or have lots of smaller servers with fewer GPUs per server. I think with the deepstream / kafka approach this doesn’t matter since the third-party tracker will consolidate anyway.

Questions:

  1. Is going for A5000s here sensible? They seem to give good performance per £. There are so many cards that it’s hard to know how to pick never mind which one.

  2. Is using multiple Deepstream instances per zone a sensible thing to do? Would one big PC be better here? One limiting factor is h264 decode, but the A5000 has two NVDEC. I think a single deepstream instance is locked to a set GPU anyway since the benefits come from the decode and inference never leaving the GPU.

  3. Should I be using the Deepstream Triton server to “pool” GPU resources somehow instead of a deepstream process per GPU?

  4. Should I be looking at something more enterprise-like with virtual GPUs?

Any input appreciated.

3

It depends on your own decision based on your requirement and resource.

Is there any technique questions?