u/Thick-Living5697

Running YOLOv11 (TensorRT) + centroid tracking on a Jetson Xavier NX (MAX-N, jetson_clocks on) for vehicle counting. Get ~27 FPS with few vehicles on screen, but it drops to 8-11 FPS when many vehicles cross at once.

Since it scales with object count, not a flat number, I suspect it's the per-object tracking/post-processing (Python-side) rather than the TensorRT inference itself.

Tried so far:

• Confirmed nvpmodel MAX-N + jetson_clocks are active
• TensorRT engine already used for inference (not raw PyTorch)
• FPS drop correlates directly with number of tracked objects on screen, not with anything else changing

Is stable 30 FPS realistic on a Xavier NX for detection + tracking + per-object logic at this object density, or should I expect this kind of drop and optimize for no dropped frames instead of a flat FPS target?

Running YOLOv11 (TensorRT) + centroid tracking on a Jetson Xavier NX (MAX-N, jetson_clocks on) for vehicle counting. Get ~27 FPS with few vehicles on screen, but it drops to 8-11 FPS when many vehicles cross at once.

Since it scales with object count, not a flat number, I suspect it's the per-object tracking/post-processing (Python-side) rather than the TensorRT inference itself.

Tried so far:

• Confirmed nvpmodel MAX-N + jetson_clocks are active
• TensorRT engine already used for inference (not raw PyTorch)
• FPS drop correlates directly with number of tracked objects on screen, not with anything else changing

Is stable 30 FPS realistic on a Xavier NX for detection + tracking + per-object logic at this object density, or should I expect this kind of drop and optimize for no dropped frames instead of a flat FPS target?