论文解读《Deep People Counting in Extremely Dense Crowds》ACM MM2015
Deep People Counting in Extremely Dense Crowds 极度密集的深度人群计数 Chuan Wang, Hua Zhang, Liang Yang, Si Liu, Xiaochun Cao 摘要： 1. 问题： 缺少训练样本、严重遮挡、杂乱的背景、视角变化 2. 提出端到端的深度CNN回归模型对极其密集人群计数。 3. 特性： 基于CNN的深度模型，可以
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相关文章
- 1. 论文解读《CrowdNet: A Deep Convolutional Network for Dense Crowd Counting》ACMM-MM2016
- 2. Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images(CVPR2013)——论文笔记
- 3. 论文解读《Almost Unsupervised Learning for Dense Crowd Counting》AAAI2019
- 4. 论文解读---Frozone: Freezing-Free, Pedestrian-Friendly Navigation in Human Crowds
- 5. 论文解读《Crowd Counting via Multi-layer Regression》ACM-MM2019
- 6. 论文理解《Body Structure Aware Deep Crowd Counting》
- 7. 论文理解《Dense crowd counting from still images with convolutional neural networks》
- 8. 人群密度估计--CrowdNet: A Deep Convolutional Network for Dense Crowd Counting
- 9. ZOJ - 3944 People Counting （模拟）
- 10. [论文解读] DSD -- Dense-Sparse-Dense Training for Neural Network