Multi Tracking Multi Camera with Re-Identification starts fast and slows down and quits

Hey @sovrasov,

I am running the demo: https://github.com/openvinotoolkit/open_model_zoo/tree/master/demos/multi_camera_multi_target_tracking_demo/python

I have run this on a core i5 with Nvidia GPU and 8 Gigs Ram as well as on a core i9 with 16 Gigs Ram with the time_window set to 1 in the configs/person.pyfile to ensure REID.

The system works great but slows to an absolute crawl within 5 minutes, like 1 frame per 10 seconds at best. The GPU/CPU and memory are not exploited, they are not effected or at a maximum (under 60% utilization) but still the system crawls.

Initially it starts off with 20 or 30 (or more) frames a second but then just bottoms out.

Utilizing the :
FP16-INT8\person-reidentification-retail-0277 (for reidentification)

and

FP16-INT8\person-detection-retail-0013.xml (for detection)

Have tried both FP16 and FP32.

When the config is set to time_window=10 then it flies but reidentification is at 10% or 20% accuracy at best, most people are not reidentified, when set to 1 second, all are identified but system crawls to a grinding halt.

Any help would be appreciated.

Thanks

Start with the multi_camera_multi_target_tracking_demo/python demo and inspect configs/person.py, especially the time_window setting. Reproduce the contrast between time_window=1 and time_window=10 using the stated re-identification and detection models, then trace where performance degrades. Done means the demo maintains usable throughput while preserving the expected re-identification behavior.