• Hardware Platform: GPU
• DeepStream Version: 5.0.0
• TensorRT Version: 22.214.171.124
• NVIDIA GPU Driver Version (valid for GPU only): 460.32.03
I would like to map a face recognition pipeline to deepstream and make extensive use of the GPU. So right now in the SDK it’s possible to provide a model to produce bounding boxes and additionally a function to parse those and forward those boxes to another element in the pipeline in charge of displaying it on that actual frame. First question is here: To where is the output of the bbox parsing before forwarded? Right now only the execution of the model is being executed on the device. I would like to have as much as possible to be running on the GPU resp. having as less as possible context switches.
What would be the best approach to get something like this mapped into DS:
Frame → Detection [PGIE] → post processing on each detected bbox → feed every post-processed finding into another model [SGIE?] → second post processing on model output and pre computed data → feed everything into another model [another SGIE?] → output (ideally here is the switch back to host)
would something like this be possible? how would i start on this?