人群计数--Switching Convolutional Neural Network for Crowd Counting

Switching Convolutional Neural Network for Crowd Counting
CVPR2017
Code for SCNN is based on Lasagne\Theano :https://github.com/val-iisc/crowd-counting-scnn

针对人群密度估计问题提出了一个 Switch-CNN网络，大的思路就是根据图像块的内容信息来选择合适的CNN网络进行人群密度估计
首先将图像分成3*3=9 个图像块，然后使用一个 CNN网络对每个图像块进行分类，看它适合使用哪个CNN网络来进行密度估计，这里提供了3个CNN网络来进行密度估计。

对于图像中的人群密度，有的区域密度大，有的区域密度小，这里我们对图像进行分块处理

Switch-CNN

这里的三个CNN网络设计R1，R2，R3 和文献【19】中的 MCNN 是一样的。
对于 switch classifier 我们以 VGG16网络为基础

Switch-CNN 的训练步骤如下所示

Ground Truth 我们采用和文献【19】一样的方法生成密度真值图，使用 Gaussian geometry-adaptive kernels

Switch-CNN 的训练包括三个步骤： pretraining, differential training and coupled training
3.2. Pretraining
就是对三个 CNN regressors R 1 through R 3 进行独立的训练， regress density maps，每个网络的训练都是在所有训练数据上进行的。
3.3. Differential Training
differential training generates three disjoint groups of training patches and each network is finetuned on its own group. The regressors R k are differentially trained until the validation accuracy plateaus.
这里我们将训练图像块进行分为三类，对应三个CNN网络需要的训练数据，然后再对每个CNN网络使用对应的数据集合进行微调

3.4. Switch Training
The classifier is trained on the labels of multichotomy generated from differential training

3.5. Coupled Training
we co-adapt the patch classifier and the CNN regressors by training the switch and regressors in an alternating fashion

4 Experiments

ShanghaiTech dataset

UCF CC 50 dataset

UCSD crowd-counting dataset WorldExpo’10 dataset

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