Geometric and Physical Constraints for Head Plane Crowd Density Estimation in Videos
創新點一： 這篇文章從解決透視畸變入手，先提出以往解決透視畸變的方法是學習具有尺度不變性的特徵和將輸入圖片分爲不同尺寸的圖像塊進行估計兩種方法。透視畸變對人羣密度估計產生的影響往往在於遠近像素代表的實際大小不同。舉個例子，遠近相同大小的兩片區域站相同數量的人，如果不考慮透視畸變，則估計出來的兩片區域的人羣密度是不同的。文章中也給出了實驗證明： a中的紅框中兩塊區域的密度是差不多的，但是真值圖b顯示
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相關文章
- 1. CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting
- 2. UniPose: Unified Human Pose Estimation in Single Images and Videos
- 3. 人羣密度估計--Spatiotemporal Modeling for Crowd Counting in Videos
- 4. 人羣分析--Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks
- 5. 姿態估計及跟蹤「Detect-and-Track: Efficient Pose Estimation in Videos」
- 6. Learning latent geometric consistency for 6D object pose estimation in heavily cluttered scenes
- 7. 論文解讀《Crowd Density Estimation Using Fusion of Multi-Layer Features》T-ITS2020
- 8. 人羣計數：SFCN--Learning from Synthetic Data for Crowd Counting in the Wild
- 9. Using XtraBackup for Physical Backup and Restoration
- 10. 論文閱讀：《Flowing ConvNets for Human Pose Estimation in Videos》ICCV 2015