I have made a custom dataset then used that to fine tune Pytorch faster RCNN model. Now I have a conveyor, on top of which I have placed a camera. I am using the image from that camera and passing it into the model for inference.The model outputs a result dictionary which contains labels, bbox coordinates, score. Since I have a continuous camera feed and objects are coming on the conveyor, I am getting multiple entries for the same object. Say if I have a can on the conveyor then I am getting atleast 10-15 entries till the can goes out of camera’s frame of view. I want to save the unique detections in an array as I have to use that data further up in the pipeline.
I am at my wits end, I have tried using Bounding Box similarity as a factor to store unique detections, I have tried assigning unique ids to the detections, I have also tried using Bounding Box corner coordinates as a way to filter out unique detections. But all of that fails if I have say 2 cans simultaneously behind each other coming in on the conveyor, then the array only saves that as 1 detection and this messes up the further pipeline. I would like to know if there is a better way to do this task or if I am doing something wrong. Any help would be much appreciated.