論文解讀《Deep People Counting in Extremely Dense Crowds》ACM MM2015
Deep People Counting in Extremely Dense Crowds 極度密集的深度人羣計數 Chuan Wang, Hua Zhang, Liang Yang, Si Liu, Xiaochun Cao 摘要： 1. 問題： 缺少訓練樣本、嚴重遮擋、雜亂的背景、視角變化 2. 提出端到端的深度CNN迴歸模型對極其密集人羣計數。 3. 特性： 基於CNN的深度模型，可以
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相關文章
- 1. 論文解讀《CrowdNet: A Deep Convolutional Network for Dense Crowd Counting》ACMM-MM2016
- 2. Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images(CVPR2013)——論文筆記
- 3. 論文解讀《Almost Unsupervised Learning for Dense Crowd Counting》AAAI2019
- 4. 論文解讀---Frozone: Freezing-Free, Pedestrian-Friendly Navigation in Human Crowds
- 5. 論文解讀《Crowd Counting via Multi-layer Regression》ACM-MM2019
- 6. 論文理解《Body Structure Aware Deep Crowd Counting》
- 7. 論文理解《Dense crowd counting from still images with convolutional neural networks》
- 8. 人羣密度估計--CrowdNet: A Deep Convolutional Network for Dense Crowd Counting
- 9. ZOJ - 3944 People Counting （模擬）
- 10. [論文解讀] DSD -- Dense-Sparse-Dense Training for Neural Network