Bottom-Up 2D Pose Estimation via Dual Anatomical Centers for Small-Scale Persons

• URL: http://arxiv.org/abs/2208.11975v1
• Date: Thu, 25 Aug 2022 10:09:10 GMT
• Title: Bottom-Up 2D Pose Estimation via Dual Anatomical Centers for Small-Scale Persons
• Authors: Yu Cheng, Yihao Ai, Bo Wang, Xinchao Wang, Robby T. Tan
• Abstract summary: In multi-person 2D pose estimation, the bottom-up methods simultaneously predict poses for all persons. Our method achieves 38.4% improvement on bounding box precision and 39.1% improvement on bounding box recall over the state of the art (SOTA) For the human pose AP evaluation, we achieve a new SOTA (71.0 AP) on the COCO test-dev set with the single-scale testing.
• Score: 75.86463396561744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In multi-person 2D pose estimation, the bottom-up methods simultaneously predict poses for all persons, and unlike the top-down methods, do not rely on human detection. However, the SOTA bottom-up methods' accuracy is still inferior compared to the existing top-down methods. This is due to the predicted human poses being regressed based on the inconsistent human bounding box center and the lack of human-scale normalization, leading to the predicted human poses being inaccurate and small-scale persons being missed. To push the envelope of the bottom-up pose estimation, we firstly propose multi-scale training to enhance the network to handle scale variation with single-scale testing, particularly for small-scale persons. Secondly, we introduce dual anatomical centers (i.e., head and body), where we can predict the human poses more accurately and reliably, especially for small-scale persons. Moreover, existing bottom-up methods use multi-scale testing to boost the accuracy of pose estimation at the price of multiple additional forward passes, which weakens the efficiency of bottom-up methods, the core strength compared to top-down methods. By contrast, our multi-scale training enables the model to predict high-quality poses in a single forward pass (i.e., single-scale testing). Our method achieves 38.4\% improvement on bounding box precision and 39.1\% improvement on bounding box recall over the state of the art (SOTA) on the challenging small-scale persons subset of COCO. For the human pose AP evaluation, we achieve a new SOTA (71.0 AP) on the COCO test-dev set with the single-scale testing. We also achieve the top performance (40.3 AP) on OCHuman dataset in cross-dataset evaluation.

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