(论文笔记2019ICCV) Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting
这篇工作是发表在2019ICCV1上的, 关于Crowd Counting的文章; 在Crowd Counting领域2, 经典的方法是采用基于目标检测的框架, 以滑动窗口的形式检测人头或者人身体的一部分, 但是随着人数越来越密集, 人与人之间的遮挡问题越来越严重, 造成基于检测的方法效果越来越差; 在现代的Crowd Counting主流方法中, 基本都是采用基于回归的方法, 通过学习特征图到人
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相关文章
- 1. [论文笔记 CVPR2020]Attention Scaling for Crowd Counting
- 2. Scale-Aware Attention Network for Crowd Counting 论文笔记
- 3. 《Multi-scale Convolutional Neural Networks for Crowd Counting》论文笔记
- 4. Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images(CVPR2013)——论文笔记
- 5. 【论文浅读】《Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs》
- 6. 人群分析--Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks
- 7. 论文笔记——Body Structure Aware Deep Crowd Counting
- 8. 人群计数论文笔记之PaDNet:Pan-Density Crowd Counting
- 9. 人群密度估计--Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs
- 10. 论文解读《Almost Unsupervised Learning for Dense Crowd Counting》AAAI2019