Greedy Offset-Guided Keypoint Grouping for Human Pose Estimation
- URL: http://arxiv.org/abs/2107.03098v1
- Date: Wed, 7 Jul 2021 09:32:01 GMT
- Title: Greedy Offset-Guided Keypoint Grouping for Human Pose Estimation
- Authors: Jia Li, Linhua Xiang, Jiwei Chen, Zengfu Wang
- Abstract summary: We employ an Hourglass Network to infer all the keypoints from different persons indiscriminately.
We greedily group the candidate keypoints into multiple human poses, utilizing the predicted guiding offsets.
Our approach is comparable to the state of the art on the challenging COCO dataset under fair conditions.
- Score: 31.468003041368814
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a simple yet reliable bottom-up approach with a good trade-off
between accuracy and efficiency for the problem of multi-person pose
estimation. Given an image, we employ an Hourglass Network to infer all the
keypoints from different persons indiscriminately as well as the guiding
offsets connecting the adjacent keypoints belonging to the same persons. Then,
we greedily group the candidate keypoints into multiple human poses (if any),
utilizing the predicted guiding offsets. And we refer to this process as greedy
offset-guided keypoint grouping (GOG). Moreover, we revisit the
encoding-decoding method for the multi-person keypoint coordinates and reveal
some important facts affecting accuracy. Experiments have demonstrated the
obvious performance improvements brought by the introduced components. Our
approach is comparable to the state of the art on the challenging COCO dataset
under fair conditions. The source code and our pre-trained model are publicly
available online.
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