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MLE-Loss Driven Robust Hand Pose Estimation
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DOI：10.1109/ACCESS.2024.3429531.png)
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Abstract
En 中文
This paper introduces a novel method for accurately estimating the 2D coordinates of hand keypoints from single static images, utilizing a sequential convolutional neural network optimized with Maximum Likelihood Estimation Loss. Unlike traditional heatmap-based techniques, our approach eliminates the need to generate label heatmaps and sidesteps the direct optimization of model parameters based on noisy labels. Instead, it concentrates on modeling the distribution of the discrepancies between predicted results and ground truth, rather than the potential presence of noisy labels, thus enabling the direct prediction of hand keypoint coordinates. Furthermore, we propose a sequential training and inference framework that consists of a deep convolutional backbone network and a multi-stage sequential network. Each stage of this network features similar structures, facilitating the progressive and precise prediction of hand keypoint coordinates. Our extensive experimental results demonstrate that our approach is both highly accurate and robust, outperforming mainstream methods under the experimental conditions detailed in this paper.
Keywords:
Heating systems
Pose estimation
Computational modeling
Training
Accuracy
Maximum likelihood estimation
Convolutional neural networks
Deep learning
Hand pose estimation
maximum likelihood estimation
heatmap
deep learning
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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