一、论文简介：

基于密集点的一个姿态估计模型

作者：

本文主要是Facebook AI 和INRIA（法国国家信息与自动化研究所）联合出品

效果：

关键点对应 2d imageà3d surface coordinate(uv坐标)

将2d图像的关键点映射成一个uv坐标，然后把这个uv坐标贴回到2d 的image上。

难点：

存在背景，遮挡，姿态，尺度的变化。应对这个问题之前的工作需要深度传感器

创新点：

1 ：标注了一个新的数据集，基于coco数据集，增加了uv标注，该数据集开源。

2：设计了一个输入一张rgb的图片输出uv坐标的网络框架。在mask rcnn的结构基础上修改的。

3 ：设计了一个“teacher”网络（用来生成数据集）。

数据集：

数据图片来源COCO2014数据集

train:26437 images

valminusminival: 5984 images

All:32421 images  48k humans

minival: 1508 images 2.3k humans

共标注了50K个人，以及超过500万的人工标注点。

teacher network:

人工标注阶段：人体共分成24块区域。每个局域通过聚类产生一些等距的关键点，关键点的数量根据区域的大小来定，最多有14个点。将这些点人工标注到3d表面（uv坐标）每个人大概有100-150个点

Teacher:训练了一个全卷积的teacher network输入人工标注的稀疏的特征点（uv坐标）输出更加密集的特征点。训练的时候是对图片根据bounding box裁剪出来人的区域，使用这个来训练网络，减少背景影响。

网络结构:

backbone network：Resnet50,Resnet101,Resnext

RPN: fpnROI-Align pooling

Head:必选的Fast-rcnn body_uv

可选的：mask、keypoint

网络结构方面的主要贡献就是body_uv branch和级联结构

分别来讲：

（1）、body_uv branch采用了和mask rcnn中的keypoint branch分支一样的网络结构（八层的同等分辨率卷积结构）后面接两个损失函数：

先对每个像素使用分类器进行分类，分类到所属于的区域（24+1（bachground））,损失函数为交叉熵损失。25路

然后使用回归器回归精确的位置，损失函数为L1损失，24路

（2）、网络的修改的小技巧：将多任务的输出进行级联，通过任务的协同作用，以及互补的不同监督信息带来的一些优点，

具体来讲就是将keypoint/mask 的输出和body_uv的输出进行融合，然后在分别输出计算损失。

效果：

一个GTX 1080 GPU

20-26 帧/s 240*320的图片

4-5帧/s 800*1100的图片

细节：

与DenseReg很像，但是DenseReg使用的是全卷积,并且DenseReg仅仅是对脸部进行预测，并且变化的幅度较小。（第一作者是同一个人）

二、实验部分:

比较了基于全卷积的神经网络（deeplab）和基于区域的网络(Mask-RCNN)，发现基于区域的网络更优

Posetract challenge：

相同的任务，又重新在posetrack数据集上标注了一份数据集

Train:1680 images

Val:782 images

Test: 2698 images

数据分析

、训练和验证

数据

COCO_Densepose

Train：

Images  annotations  categories

小训练集的图片数:26437

图片的的字典:[u'license', u'file_name', u'coco_url', u'height', u'width', u'date_captured', u'flickr_url', u'id']

图片字典举例:{u'license': 2, u'file_name': u'COCO_train2014_000000262145.jpg', u'coco_url': u'http://images.cocodataset.org/train2014/COCO_train2014_000000262145.jpg', u'height': 427, u'width': 640, u'date_captured': u'2013-11-20 02:07:55', u'flickr_url': u'http://farm8.staticflickr.com/7187/6967031859_5f08387bde_z.jpg', u'id': 262145}

小训练集的标注文件数:100403

标注文件的字典:[u'segmentation', u'num_keypoints', u'dp_masks', u'area', u'iscrowd', u'dp_I', u'keypoints', u'id', u'dp_U', u'image_id', u'dp_V', u'bbox', u'category_id', u'dp_y', u'dp_x']

关键字:segmentation长度:1

关键字:num_keypoints值:15

关键字:dp_masks长度:14

关键字:area值:21258

关键字:iscrowd值:0

关键字:dp_I长度:115

关键字:keypoints长度:51

关键字:id值:1218400

关键字:dp_U长度:115

关键字:image_id值:262145

关键字:dp_V长度:115

关键字:bbox长度:4

关键字:category_id值:1

关键字:dp_y长度:115

关键字:dp_x长度:115

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模型的输出:

Body_uv:

<type 'list'>

子列表总数为:12158

body_uv的结果文件的关键字:[u'image_id', u'category_id', u'uv', u'score', u'bbox']

uv数据:(3, 224, 44)

body_uv的结果文件举例:{u'image_id': 885, u'category_id': 1, u'uv': array([[[0, 0, 0, ..., 0, 0, 0],

        [0, 0, 0, ..., 0, 0, 0],

        [0, 0, 0, ..., 0, 0, 0],

        ...,

        [0, 0, 0, ..., 0, 0, 0],

        [0, 0, 0, ..., 0, 0, 0],

        [0, 0, 0, ..., 0, 0, 0]],

       [[0, 0, 0, ..., 0, 0, 0],

        [0, 0, 0, ..., 0, 0, 0],

        [0, 0, 0, ..., 0, 0, 0],

        ...,

        [0, 0, 0, ..., 0, 0, 0],

        [0, 0, 0, ..., 0, 0, 0],

        [0, 0, 0, ..., 0, 0, 0]],

       [[0, 0, 0, ..., 0, 0, 0],

        [0, 0, 0, ..., 0, 0, 0],

        [0, 0, 0, ..., 0, 0, 0],

        ...,

        [0, 0, 0, ..., 0, 0, 0],

        [0, 0, 0, ..., 0, 0, 0],

        [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8), u'score': 0.9986090064048767, u'bbox': [594.8278198242188, 26.12091064453125, 44, 224]}

该图片为:

图片的大小:(427, 640, 3)

visualize the I, U and V images.:

visualize the isocontours of the UV fields:

u'uv'输出：

(3, 224, 44)

u的总坐标数:9856

u的有效坐标数:4281（数组其余元素都是0）

结论：

uv数据（三维数组）的大小是bounding box一样的。

输出的uvi数组（(3, 224, 44)三维分别是u、v、I,和bbox一样大小的宽和高是由输出的56*56的feature map 通过cv2.resize()得到的，也就是线性插值得到的。）

每个index的那部分数据，是由相应通道的feature中的那一小块数据得到的

uv坐标数据分析

（1）（训练数据）提供了dp_x, dp_y, dp_I, dp_U , dp_V

dp_x, dp_y: 是人工标注者收集的点在图像中的空间坐标

dp_I : 代表每个点所属的24块区域中的一块

dp_U , dp_V: 是uv空间空间中的坐标，每一块模型的表面都有一个这样的二维参数数量在100左右

举例：

第一个图根据dp_I划分的每个点所属的区域

第二个，第三个图是使用x，y定位点的位置，然后分别使用u和v的值渲染出来的颜色。

（2）(模型输出结果)

模型输出结果是一个包含i，u，v值的mask, mask大小和人物的bbox一样大，这里面的u和v值就是密集的值了，数量是万级别的。也就是论文里的密集对应。

输出结果渲染结果展示：

（3）模型的损失计算

分类的softmax损失，和关键点的L1损失

（4）模型的评测标准

Body_uv评测使用OGPS作为度量，使用AP作为标准

OGPS:（1）计算预测的关键点与标签数据的关键点中距离最近的一对点。

（2）计算这对点的距离。

训练数据处理流程：

将输出的（56*56）的feature map 根据x y index 信息双线性差值池化成196*25形状的数组，然后与标签的u、v的196*25的数组做损失，标签本身的body_u,body_v的大小是196*1，然后复制25遍变成196*25

（二）、文件简介

预训练参数文件：detectron-download-cache
detectron-output：训练保存的参数的文件夹
configs：网络配置文件夹
detectron：detectron平台核心代码文件夹
tools：训练、测试、推断的python脚本
sh：训练、测试的shell脚本

（三）、修改的文件

config.py

增加了nonlocal板块

dataset_catalog.py

增加了测试集COCO_test2015

task_evaluation.py

body_uv_rcnn_heads.py

增加了一系列的头

解耦：add_roi_body_uv_head_v1convX_Decoupling

仿resnet结构：add_roi_body_uv_head_Modification_resnet

fast_rcnn_heads.py

增加了一个卷积加全链接的头（卷积共用，全链接只有分类用）

头：add_roi_Xconv1fc_gn_head_test

配合用的输出：add_fast_rcnn_outputs_test

model_builder.py

若更换FAST的head需要在287行替换

fast_rcnn_heads.add_fast_rcnn_outputs_test(model, blob_frcn, dim_frcn)

如果使用解耦需要修改355行的_add_roi_body_uv_head

函数内部分内容

nonlocal_helper.py

为增加的nonlocal部分

ResNet_nonlocal.py

为添加了nonlocal的ResNet基础网络

四、实验（以ResNet50为backbone）

（一）、修改body_uv_head，增深了网络层数，24层的Residual structure，并且使用了GN，8%的提升

（二）、在ResNet50的第三个stage的第1,3residual_block后面和第四个stage的第1,3,5个residual_block后面添加nonlocal结构，可以带来1.3%-1.7%的提升

（三）、将fast_head由原来的两层全连接结构替换为，共用四层的卷积，然后分类分支再增加两层的全连接的结构，有0.5的提升

（四）、将原来的body_uv_head八层的卷积解耦，ann和index一个分支，u,v一个分支，每个分支八层的卷积，带来1.4%的提升

（五）、同步缩小训练时的图像尺寸，同步缩小64像素，单尺度测试，带来0.8%的提升

（六）、测试增强，SCALES: (400, 500, 600, 700, 900, 1000, 1100, 1200)有2%的提升

（七）、Multi-task，keypoint分支和body_uv分支联合训练，大约带来2%的提升

论文：DensePose: Dense Human Pose Estimation In The Wild 阅读笔记

数据

（二）、文件简介

（三）、修改的文件

四、实验（以ResNet50为backbone）

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