Distribution-Aware Coordinate Representation for Human Pose Estimation
| Autor: | Hanbin Dai, Xiatian Zhu, Ce Zhu, Mao Ye, Feng Zhang |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Process (computing) Pattern recognition 02 engineering and technology 010501 environmental sciences 01 natural sciences Object detection 0202 electrical engineering electronic engineering information engineering Code (cryptography) 020201 artificial intelligence & image processing Artificial intelligence Representation (mathematics) business Pose 0105 earth and related environmental sciences |
| Zdroj: | CVPR |
| DOI: | 10.1109/cvpr42600.2020.00712 |
| Popis: | While being the de facto standard coordinate representation in human pose estimation, heatmap is never systematically investigated in the literature, to our best knowledge. This work fills this gap by studying the coordinate representation with a particular focus on the heatmap. Interestingly, we found that the process of decoding the predicted heatmaps into the final joint coordinates in the original image space is surprisingly significant for human pose estimation performance, which nevertheless was not recognised before. In light of the discovered importance, we further probe the design limitations of the standard coordinate decoding method widely used by existing methods, and propose a more principled distribution-aware decoding method. Meanwhile, we improve the standard coordinate encoding process (i.e. transforming ground-truth coordinates to heatmaps) by generating accurate heatmap distributions for unbiased model training. Taking the two together, we formulate a novel Distribution-Aware coordinate Representation of Keypoint (DARK) method. Serving as a model-agnostic plug-in, DARK significantly improves the performance of a variety of state-of-the-art human pose estimation models. Extensive experiments show that DARK yields the best results on two common benchmarks, MPII and COCO, consistently validating the usefulness and effectiveness of our novel coordinate representation idea. Results on the COCO keypoint detection challenge: 78.9% AP on the test-dev set (Top-1 in the leaderbord by 12 Oct 2019) and 76.4% AP on the test-challenge set. Project page: https://ilovepose.github.io/coco |
| Databáze: | OpenAIRE |
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