Closed minhhoangbui closed 3 years ago
Dear minhhoangbui,
Answering your questions:
1) The Bounding Box detection is obtained by the adding heatmap layer for center and corner coordinates of the bounding box in the same head of the of the pose estimation.
2) Since UniPose is intended for single person pose estimation, and the MPII dataset is a multi-person dataset, we initially identified the target individual for the detection using the center location provided. We also performed segmentation with our WASP method: https://www.mdpi.com/1424-8220/19/24/5361, but those results aren't included in this paper.
@bmartacho
Thank you a lot for this answer. I'm a bit curious. Is it necessary to predict background heatmap. According to my understanding about top-down pose architecture, not many provide this.
Furthermore, I think top-left point and bottom-right is enough to define a bounding box. Why you need 5 points in this case ?
Another thing, the way you name variable limbs
in heat, limbs = self.model(input_var)
may be a little misleading.
Hello,thanks for your discussion. I have a question about the evaluation of the Penn Action dataset. The paper said the evaluation follows [27], i.e. LSTM Pose Machines, so the torso is represented by the size of the bbox, right? But in the file evaluate.py
, the torso was calculated by the l2 norm of the difference between the neck and the pelvis. I'm a little confused about it. Could you give me some suggestions, please? @minhhoangbui @bmartacho Thanks very much!
Dear, Thank you a lot for your work. And I have some questions to ask: 1) I don't see you train the person detection branch. How come you can predict bbox and pose at the same time as you claim? 2) In MPII, you use
segmented
folder. How can you find that annotation? I'm looking forward to hearing from you