Geometric and Physical Constraints for Head Plane Crowd Density Estimation in Videos
创新点一： 这篇文章从解决透视畸变入手，先提出以往解决透视畸变的方法是学习具有尺度不变性的特征和将输入图片分为不同尺寸的图像块进行估计两种方法。透视畸变对人群密度估计产生的影响往往在于远近像素代表的实际大小不同。举个例子，远近相同大小的两片区域站相同数量的人，如果不考虑透视畸变，则估计出来的两片区域的人群密度是不同的。文章中也给出了实验证明： a中的红框中两块区域的密度是差不多的，但是真值图b显示
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相关文章
- 1. CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting
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- 4. 人群分析--Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks
- 5. 姿态估计及跟踪“Detect-and-Track: Efficient Pose Estimation in Videos”
- 6. Learning latent geometric consistency for 6D object pose estimation in heavily cluttered scenes
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- 8. 人群计数：SFCN--Learning from Synthetic Data for Crowd Counting in the Wild
- 9. Using XtraBackup for Physical Backup and Restoration
- 10. 论文阅读：《Flowing ConvNets for Human Pose Estimation in Videos》ICCV 2015