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HandyPose: Multi-level framework for hand pose estimation
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DOI：10.1016/j.patcog.2022.108674.png)
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摘要
En 中文
Hand pose estimation is a challenging task due to the large number of degrees of freedom and the frequent occlusions of joints. To address these challenges, we propose HandyPose, a single-pass, end -to-end trainable architecture for 2D hand pose estimation using a single RGB image as input. Adopt-ing an encoder-decoder framework with multi-level features, along with a novel multi-level waterfall atrous spatial pooling module for multi-scale representations, our method achieves high accuracy in hand pose while maintaining manageable size complexity and modularity of the network. HandyPose takes a multi-scale approach to representing context by incorporating spatial information at various levels of the network to mitigate the loss of resolution due to pooling. Our advanced multi-level waterfall module leverages the efficiency of progressive cascade filtering while maintaining larger fields-of-view through the concatenation of multi-level features from different levels of the network in the waterfall module. The decoder incorporates both the waterfall and multi-scale features for the generation of accurate joint heatmaps in a single stage. Our results demonstrate state-of-the-art performance on popular datasets and show that HandyPose is a robust and efficient architecture for 2D hand pose estimation.(c) 2022 Elsevier Ltd. All rights reserved.
Keyword:
Hand pose estimation
Feature representations
Computer vision
AI总结
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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引用论文
Attention! A Lightweight 2D Hand Pose Estimation Approach注意!一种轻量级的二维手姿态估计方法.png)
IEEE SENSORS JOURNALOAAI
IEEE SENSORS JOURNALIF4.5
OAAI分享收藏Fast and Accurate 3D Hand Pose Estimation via Recurrent Neural Network for Capturing Hand ArticulationsIEEE ACCESS
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