人群密度估计--CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

CrowdNet: A Deep Convolutional Network for Dense Crowd Counting
published in the proceedings of ACM Conference on Multimedia (ACMMM) - 2016
http://val.serc.iisc.ernet.in/CrowdNet/
Caffe: https://github.com/davideverona/deep-crowd-counting_crowdnet

针对人群密度估计问题，本文使用 deep and shallow, fully convolutional networks 两个网络相结合实现 large scale variations，
high-level semantic information (face/body detectors) and the low-level features (blob detectors)

我们的网络结构如下所示：

Deep Network 主要用捕获 high-level semantics 信息，这里我们采用一个类似 VGG网络的结构，我们去掉了全连接层，网络变成了全卷积层。同时原来的 VGG网络使用了5个 max-pool layers each with a stride of 2，最终的特征图大小只有输入图像尺寸的1/32。我们这里需要输出像素级别的人群密度估计图，所以我们 set the stride of the
fourth max-pool layer to 1 and remove the fifth pooling layer，这样最终的特征图大小只有输入图像尺寸的 1/8.

the receptive-field mismatch caused by the removal of stride in the fourth max-pool layer
将第四最大池化层的步长设置为1会导致 the receptive-field mismatch，这里我们使用了文献【4】中的膨胀卷积。其结果就相当第四最大池化层的步长设置为2

Shallow Network
这里我们使用一个 shallow convolutional network 主要用于检测远离相机的人头， used for the detection of small head-blobs

Combination of Deep and Shallow Networks
这里 concatenate Deep and Shallow Networks 的输出，输入图像尺寸的 1/8，使用一个 1x1 convolution layer，再 upsampled to the size of the input image using bilinear interpolation to obtain the final crowd density prediction

3.2 Ground Truth
generate our ground truth by simply blurring each head annotation using a Gaussian kernel normalized to sum to one

3.3 Data Augmentation
这里主要使用两类数据增强
primarily perform two types of augmentation
1）对 scale variations 我们多尺度采样

2）对容易错误的样本我们多训练几次
sampling high density patches more often

4 EXPERIMENTS

train.txt 内容
/media/guest/新加卷/spgz/perDensity/Subway/train_test/shanghai/crop/dcc_crowdnet/train_0.h5
/media/guest/新加卷/spgz/perDensity/Subway/train_test/shanghai/crop/dcc_crowdnet/train_7000.h5
/media/guest/新加卷/spgz/perDensity/Subway/train_test/shanghai/crop/dcc_crowdnet/train_14000.h5

test.txt 内容
/media/guest/新加卷/spgz/perDensity/Subway/train_test/shanghai/crop/dcc_crowdnet/test_0.h5

人群密度估计--CrowdNet: A Deep Convolutional Network for Dense Crowd Counting相关推荐

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 ...
【Cascade FPD】《Deep Convolutional Network Cascade for Facial Point Detection》
CVPR-2013 文章目录 1 Background and Motivation 2 Related Work 3 Advantages / Contributions 4 Method 5 Ex ...
(SRCNN)及pytorch实现_Learning a Deep Convolutional Network for Image Super-Resolution——超分辨率（二)
文章目录 Learning a Deep Convolutional Network for Image Super-Resolution 算法简介算法流程 Patch extraction and ...
图像超分辨率之SRCNN（Learning a Deep Convolutional Network for Image Super-Resolution）
论文下载:Learning a Deep Convolutional Network for Image Super-Resolution 代码下载:https://github.com/tegg89 ...
Ensemble of the Deep Convolutional Network for Multiclass of Plant Disease Classi¯cation Using Leaf
Ensemble of the Deep Convolutional Network for Multiclass of Plant Disease Classi¯cation Using Leaf ...
《Multi-scale Convolutional Neural Networks for Crowd Counting》论文笔记
Multi-scale Convolutional Neural Networks for Crowd Counting 论文地址论文翻译 ABSTRACT 1. INTRODUCTION 2. M ...
人群密度估计--Learning a perspective-embedded deconvolution network for crowd counting
Learning a perspective-embedded deconvolution network for crowd counting 没有找到代码本文在人群密度估计这个问题上的创新点: ...
2017_Switching convolutional neural network for crowd counting
Switching convolutional neural network for crowd counting 说明概括一.Switch-CNN简介二.CrowdNet[2]和MCNN[3] ...
Multi-Scale Attention Network for Crowd Counting：用于人群计数的多尺度注意网络
Multi-Scale Attention Network for Crowd Counting:用于人群计数的多尺度注意网络 Multi-Scale Attention Network for Cr ...

人群密度估计--CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

人群密度估计--CrowdNet: A Deep Convolutional Network for Dense Crowd Counting相关推荐

最新文章

热门文章