人群密度估计--Fully Convolutional Crowd Counting On Highly Congested Scenes

Fully Convolutional Crowd Counting On Highly Congested Scenes
The 12th International Conference on Computer Vision Theory and Applications (VISAPP)
VISAPP 2017

本文使用 FCN 来做人群密度估计，主要参考 Single-image crowd counting via multi-column convolutional neural network，
主要改进的地方有以下三点：1）新的数据增强方法用于增加训练数据；2）提出一个更好的FCN网络；3）输入图像的多尺度用于解决 scale and perspective

FCN 用于人群计数主要的优点就是输入图像尺寸可以是任意的，输出的密度图根据输入尺寸自适应变化。目前已有的数据库主要的标记信息是人头位置标记。这里主要是根据人头位置信息得到人群密度真值图。具体的生成过程主要是： N head annotations 根据人头位置生成 discrete density heatmap，对每个人头位置加一个 unit impulse 到 discrete density heatmap 中去。

为了将离散密度能量图转为连续的函数，我们对每个人头位置使用一个adaptive Gaussian kernel 卷积
To convert this discrete density heatmap to a continuous function, convolution with an adaptive Gaussian kernel G σi is applied for each head annotation

2.1 Training Set Augmentation Scheme
由于大多数人群计数方面的数据库规模都比较小，所以数据增强计数就显得比较重要。

allow these crops to overlap for image recognition tasks, pixel-wise tasks can potentially overfit
从图像中裁出的图像块最好不要有重叠
所以我们采取了 four image quadrants as well as their horizontal flips are taken as training samples, ensuring no overlap

2.2 FCN Architecture

loss function

Shanghaitech Part B validation performance

2.3 Multi-Scale Averaging During Inference
我们对输入的测试图像使用多尺度输入，得到更好的密度估计，original size + 80% original size

UCF CC 50 dataset

Shanghaitech dataset

Cross dataset performance of our method

人群密度估计--Fully Convolutional Crowd Counting On Highly Congested Scenes相关推荐

人群密度估计--CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes CVPR2018 ...
论文学习笔记：CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
CSRNet是2018年提出来的人群计数模型,其论文发表于CVPR会议. 论文链接:CSRNet Abstract 摘要我们提出了一个拥挤场景识别网络CSRNet,它提供了一种数据驱动的深度学习方法 ...
2018_Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes
Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes 说明概括一. ...
CRSNet: Dilated Convolutional Neural Networks for Underatanding the Highly Congested Scenes
CRSNet: Dilated Convolutional Neural Networks for Underatanding the Highly Congested Scenes 针对复杂场景拥挤 ...
快速人群密度估计--Multi-scale Convolutional Neural Networks for Crowd Counting
Multi-scale Convolutional Neural Networks for Crowd Counting https://arxiv.org/abs/1702.02359 对于人群密度 ...
人群分割--Fully Convolutional Neural Networks for Crowd Segmentation
Fully Convolutional Neural Networks for Crowd Segmentation https://arxiv.org/abs/1411.4464 这里设计了一个全卷 ...
人群密度估计--Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs
Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs ICCV2017 针对人群密度估计问题,本文主要从 i ...
人群计数：CSRNet-Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
(鉴于有朋友提到模型去哪里下载的问题,这里多加一句) CSRNet 官方GitHub地址:https://github.com/leeyeehoo/CSRNet-pytorch 这里面包括代码和训练好 ...
论文解读 CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
abstract 1.提出CSRNet是为了处理非常密集的场景,提供准确的计数和密度图 2.提出的CSRNet主要两部分组成:提取二维特征的CNN做前端,膨胀的CNN做后端,膨胀的卷积核是为了获得更大 ...

人群密度估计--Fully Convolutional Crowd Counting On Highly Congested Scenes

人群密度估计--Fully Convolutional Crowd Counting On Highly Congested Scenes相关推荐

最新文章

热门文章