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I'm unable to comprehend the reason to clip the similarity to zero if the (head normalized) distance is greater than 0.5.
This has consequences in (incorrectly) tagging each detection as TP/TN/FP/FN for each alpha ranging from 0.05 to 0.45 at
However, when we try to estimate HOTA at different confidences (0.05 to 0.99), the clipping of similarity has effect of completely neglecting all the [email protected], @[email protected], @0.2, @0.25 @0.3 @0.35 @0.4 aand 0.45.
Futher it additionally calculates [email protected], @0575, @0.625, @0.675 @0.725, @0.775 @0.825 @0.875 @0.925 @0.975 to the original [email protected], @055, @0.6, @0.65 @0.7, @0.75 @0.8 @0.85 @0.9 @0.95
To connect the things into perspective, pascal VOC computes [email protected] whereas COCO computes the [email protected]:0.99:0.05. MOTA calculates [email protected] and hota calculates at all levels @0.05:0.99:0.05.
In short, the distance function may needs to be revisited. I think sim = np.maximum(-1 * distance_mtx + 1, 0) makes it consistent with HOTA metrics based on IoU, MOTA at [email protected].
Please let me know your thoughts and did i missed something?
The text was updated successfully, but these errors were encountered:
Thanks for your feedback. I will have a closer look at this problem and I agree that sticking to [email protected]:0.99:0.05 would have been more consistent with existing evaluation metrics.
In the distance to similarity computation at
PoseTrack21/eval/posetrack21/posetrack21/trackeval/metrics/hota_pose.py
Line 37 in 35bd703
I'm unable to comprehend the reason to clip the similarity to zero if the (head normalized) distance is greater than 0.5.
This has consequences in (incorrectly) tagging each detection as TP/TN/FP/FN for each alpha ranging from 0.05 to 0.45 at
PoseTrack21/eval/posetrack21/posetrack21/trackeval/metrics/hota_pose.py
Line 147 in 35bd703
In MOTA calculation, the threshold alpha is fixed at 0.5 i.e., [email protected]. Please refer to
https://github.com/leonid-pishchulin/poseval/blob/4258a1575b9f2ddd0bdb85f74557235ab5df0f52/poseval/evaluatePCKh.py#L56.
However, when we try to estimate HOTA at different confidences (0.05 to 0.99), the clipping of similarity has effect of completely neglecting all the [email protected], @[email protected], @0.2, @0.25 @0.3 @0.35 @0.4 aand 0.45.
Futher it additionally calculates [email protected], @0575, @0.625, @0.675 @0.725, @0.775 @0.825 @0.875 @0.925 @0.975 to the original [email protected], @055, @0.6, @0.65 @0.7, @0.75 @0.8 @0.85 @0.9 @0.95
To connect the things into perspective, pascal VOC computes [email protected] whereas COCO computes the [email protected]:0.99:0.05. MOTA calculates [email protected] and hota calculates at all levels @0.05:0.99:0.05.
In short, the distance function may needs to be revisited. I think
sim = np.maximum(-1 * distance_mtx + 1, 0)
makes it consistent with HOTA metrics based on IoU, MOTA at [email protected].Please let me know your thoughts and did i missed something?
The text was updated successfully, but these errors were encountered: