Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU) GPU
• DeepStream Version 6.2
• JetPack Version (valid for Jetson only)
• TensorRT Version
• NVIDIA GPU Driver Version (valid for GPU only)
• Issue Type( questions, new requirements, bugs)
• How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing)
• Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description)
Hello, I currently configured the pipeline with
Src - pgie (seg) - nvsegvisual - windowsink. Looking at the input and output configurations (Type), I think I can put nvtracker between pgie and segvisual,
- Is this put in
- If so, I wonder if the workflow works normally
Oh, when I put it in myself, it’s not normally done with segment meta.
Then, do nvidia have any plans?
Since sam-track is very impressive, I think it would be nice to come out separately in deepstream.
Thanks for your suggestion and we will discuss that.
Hi @yeongjae1 , could you provide us with the model of this requirement and describe the inputs and outputs of this model? We can try to integrate it in DeepStream.
The inputs of the model are (H, W, C) numpy array → image frame
and outputs are (H, W) numpy array → pred mask
Please refer to the answer above. Is there anything else you need?
They use DeAOT (Decoupling features in Associating Objects with Transformers) (NeurIPS2022) algorithm for efficient multi-object tracking and propagation.
OK. There are a few questions I would like to know. Are there any trained models for this can be used now? As the outputs are (H,W) pred mask, how is the trackID of the pred mask generated?
Okay, we will discuss how to adapt that to our deepstream. If there is any progress, we will inform you in time.
Hi @yeongjae1 , could you help confirm the following points?
- You can refer to the link below to describe your whole pipeline in detail. <src, preprocess,pgie,postprocess, tracker…>.
- You need to convert the pytorch model into the onnx model.
- We can try to intergrate the model to the deepstream with a similar pipeline.
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