Open lpkoh opened 2 years ago
We have released a TensorRT + C++ implementation of ByteTrack and the speed is much faster.
We have released a TensorRT + C++ implementation of ByteTrack and the speed is much faster.
Hi yes, thank you for that, I will give it a try.
I was looking more to find out what you mean when you say "fps" in the palace video output. Is "fps" mean the entire process from video loading to preprocessing to detection to tracking, or is it referring just to time taken for tracking?
We have released a TensorRT + C++ implementation of ByteTrack and the speed is much faster.
Hi yes, thank you for that, I will give it a try.
I was looking more to find out what you mean when you say "fps" in the palace video output. Is "fps" mean the entire process from video loading to preprocessing to detection to tracking, or is it referring just to time taken for tracking?
I actually have the same question. It's unclear whether the FPS value is related to the execution of the whole pipeline or just a specific step of it (which would make no sense, in my opinion). I'm running ByteTracker as a part of a much bigger project and the code is heavily modified, but on my V100 I get something like 3-4 FPS max to do from step 2 to 5 (so measured starting from the frame acquisition to the generation of the final result).
if I understand your question the FPS just for part tracking not inclue detection (i.e not inclue yolox)
Thank you so much for this. This repo is amazing.
Can I clarify about the fps numbers being declared?
As I understand, when I run ./demo_track currently, the process is something like:
Also, in the base demo, the yolox model is not converted to tensorrt, neither is the tracker right? Does this mean we can increase fps relative to what is shown on the video output from demo by:
Your clarification would be super useful.