A Light CNN based Method for Hand Detection and Orientation Estimation
Abstract 最近，使用CNN模型的物体检测方法以高计算费用显着提高了手检测的准确性。 在本文中，我们提出了一个轻型CNN网络，它使用改进的MobileNet作为SSD框架的特征提取器，以实现对手部位置和方向的稳健和快速检测。 网络生成一组不同分辨率的特征图，以检测不同尺寸的手。 为了提高健壮性，我们还采用了一种自顶向下的功能融合体系结构，该体系结构集成了跨功能级别的上下文信息。 为了准确估计
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相关文章
- 1. A Robust Hand Detection Method Based on Skin Probability Map
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- 4. HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation
- 5. 学习总结《HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation》
- 6. 《A Multilayer Fusion Light-Head Detector for SAR Ship Detection》
- 7. 论文A CNN-based prototype method of unstructured surgical state perception and navigation
- 8. 手势估计- Hand Pose Estimation
- 9. CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting
- 10. Region-Based Convolutional Networks for Accurate Object Detection and Segmentation