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Error Compensation Heatmap Decoding for Human Pose Estimation
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AI
DOI：10.1109/ACCESS.2021.3105553.png)
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Abstract
En 中文
As a fundamental component of heatmap-based human pose estimation methods, heatmap decoding is to transform heatmaps into joint coordinates. We found that previous heatmap decoding methods generally ignored the effect of systematic errors introduced by the resolution increaseing operations in the network decoder. This work fills the gap by taking the systematic errors in heatmap decoding into consideration. We proposed a fast method to reduces the systematic and random errors in one shot by error compensation. The proposed method outperforms the previous best method on the COCO and the MPII datasets while being over 2 times faster. Extensive experiments with different networks, resolutions, metrics and datasets have proved the rationality of the proposed idea.
Keywords:
Heating systems
Optimized production technology
Decoding
Error compensation
Pose estimation
Systematics
Gaussian distribution
Human pose estimation
heatmap
decoding
error compensation
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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Cited Papers
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