PaddleDetection VS multi-object-tracker

Our RTDETR-L achieves 53.0% AP on COCO val2017 and 114 FPS on T4 GPU, while RT-DETR-X achieves 54.8% AP and 74 FPS, outperforming all YOLO detectors of the same scale in both speed and accuracy. Furthermore, our RTDETR-R50 achieves 53.1% AP and 108 FPS, outperforming DINO-Deformable-DETR->R50 by 2.2% AP in accuracy and by about 21 times in FPS. Source code and pretrained models will be available at PaddleDetection1 (https://github.com/PaddlePaddle/PaddleDetection) .

In order to make it easier for everyone to use the YOLO series model, we have open-sourced this collections. You can experience PP-YOLOE+, YOLOv8, RTMDet, DAMO-YOLO, YOLOv7, YOLOv6, YOLOX, YOLOv5...just in https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/docs/feature_models/PaddleYOLO_MODEL_en.md

Code for https://arxiv.org/abs/2203.16250 found: https://github.com/PaddlePaddle/PaddleDetection

Github: https://github.com/PaddlePaddle/PaddleDetection

You may find this useful: https://adipandas.github.io/multi-object-tracker/

As you can see, in Frame 43, the tracker assigns the ID 10 to a metal post, but a few frames later when the tracker tracks the same metal post, it provides an ID of 11. This shows that the tracker is assigning new IDs to the same detected object. I cannot seem to find out why this happens, and how to fix it. I am using the motrackers module from this repo. I have not made any changes as such in the mot_yolov3.py file, I have just added the line to print the frame counter.

I have a simple python project which has gone through at least 5 iterations since I began working on it. Please see this link: adipandas/multi-object-tracker.