NikoRohr comments

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                                            NikoRohr

Recall less than 0.001 by using ground truth as detection

> Are you sure the ground truth boxes loaded from the file are in the right format? You can check it in `prepare_data` function in `CocoVideoDataset`. It seems that everything...

Recall less than 0.001 by using ground truth as detection

I changed it in `mot_challenge_dataset.py` but now I get the error: ``` Traceback (most recent call last): File "../mmtracking/tools/test.py", line 225, in main() File "../mmtracking/tools/test.py", line 215, in main metric...

Recall less than 0.001 by using ground truth as detection

> The `image_scale` has little influence on the performance. I guess it's the fault of `CLASSES` which you forget to change it to the true class you want to evaluate....

Recall less than 0.001 by using ground truth as detection

Hey, I am still working on this issue. I tried another SORT implementation with the exact same detection files and getting reasonable results. So it has to be something with...

Recall less than 0.001 by using ground truth as detection

Are there any thresholds specifying ratio, width or height of considered bboxes? I have small bboxes with width in maximum 250 and height 130. Compared to the MOT17 dataset it...

Using SORT when the number of elements to track is constant and known - ideas to recover missidentification and missmatches

Hello, i am working on an similar project and wonder if you have found a solution in the meantime? I also had the idea to do something like deepsort with...