Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields ∗

只找一个limb,即只考虑a single pair of parts j1 and j2, 此时对应到：带权重二分图的匹配问题，权重就是积分出来的E，目标就是找到累积权重最大的匹配

When it comes to finding the full body pose of multiple

people, determining Z is a K-dimensional matching problem.

This problem is NP Hard [32] and many relaxations

exist. In this work, we add two relaxations to the optimization,

specialized to our domain. First, we choose a minimal

number of edges to obtain a spanning tree skeleton of human

pose rather than using the complete graph, as shown in

Fig. 6c. Second, we further decompose the matching problem

into a set of bipartite matching subproblems and determine

the matching in adjacent tree nodes independently,

as shown in Fig. 6d.

Our optimization scheme over

the tree structure is orders of magnitude faster than the optimization

over the fully connected graph 效率提高了几个数量级

PCKh: 各个part落在groundtruth附近，若在head size之内，认为检测出了。PCK的阈值是max(height,width) of body 的0.1 or 0.2倍。同时还可以分析PCKh-0.5等等精度的准确率

mAP

OKS: object keypoint similarity: It is calculated from scale of the person and the

distance between predicted points and GT points.

It is noteworthy

that our method has lower accuracy than the top-down

methods on people of smaller scales (APM). The reason is

that our method has to deal with a much larger scale range

spanned by all people in the image in one shot. In contrast,

top-down methods can rescale the patch of each detected

area to a larger size and thus suffer less degradation

at smaller scales.

If we use the GT bounding box and a single

person CPM [31], we can achieve a upper-bound for

the top-down approach using CPM, which is 62.7% AP.

If we use the state-of-the-art object detector, Single Shot

MultiBox Detector (SSD)[16], the performance drops 10%.

This comparison indicates the performance of top-down approaches

rely heavily on the person detector就是说自顶向下的方法每次都处理一个人，需要有个bounding box把人框出来(1. person detection 2. CPM或其他单人姿态检测算法)，然后对框出来的单个人图像进行尺度调整，到一个合适的图像大小，再进行处理。当bounding box精度不够时，自顶向下的方法误差会很高。

9.