Summary: Filters out low-confidence bounding box detections during top-down pose estimation to reduce false-positives

I'm working on a project where the animals frequently leave the field of view for extended periods. While I found that top-down pose estimation worked well when both of my animals were in frame, it also resulted in false-positives when less than the max number of animals were in frame. For example, when only one of the two animals was in frame, I would see the scenario shown in the attached image, where two bounding boxes would be placed over the same individual and some keypoints would be detected twice. Upon further investigation, I found that the "bad" bounding boxes almost always had very low detection confidences. So I implemented a new RemoveLowConfidenceBoxes (inheriting from the PostProcessor class) in postprocessor.py, and integrated it into the build_detector_postprocessor function. Now detections with low confidence are removed before reaching the pose estimation phase, preventing the issue. Feedback and suggestions welcome!