I took the code here:
We trained it for our office colleagues’ faces, and were seeing decent performance.
But, as soon as I create 2 parallel camera streams (simply creating 2 instances for 2 different cameras), and put the tensorrt inferencing inside a for loop, I see that it cannot anymore recognise more than 1 person in the same camera stream.
Single camera with any number of people
Multiple camera with each camera having only one face
What doesn’t work:
Multiple camera with any one camera having more than one face detected.
“lab” variable shows “-1” for all the recognition labels except the first one.
I have tried it on saved video file to be sure, and for same instance, single source setup is able to classify all the detections.
Also, when I print the confidence scores from “lab” variable, in the scenario where multiple faces are detected, their sum seems to be adding up to nearly 100%. It seems that the network is applying some sort of softmax across all the detections, even if they are non-overlapping.