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目前最为常用的就是OKS（Object Keypoint Similarity）指标，这个指标启发于目标检测中的 IoU指标，目的就是为了计算真值和预测人体关键点的相似度。 OKS_ {p} = \frac {\sum_i exp\ {-d_ {pi}^2 / 2S_p^2 \sigma_i^2\}\delta (v_ {pi}=1)} {\sum_i \delta (v_ {pi}=1)} 其中： p 表示groudtruth中，人的id. i 表示keypoint的id. d_ {pi} 表示groudtruth中每个人和预测的每个人的关键点的欧氏距离.

OKS是所有(标记的)目标关键点的平均关键点相似度。下面我们绘制了假设每个对象有10个独立关键点，且k_i=2\sigma_i 的情况下的预测OKS值分布(蓝色曲线)，以及双重注释数据上人类OKS值的实际分布(绿色曲线)。

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May 13, 2019 · 1、OKS（Object Keypoint Similarity） 主要指标就是OKS（Object Keypoint Similarity），这个指标启发于目标检测中的IoU指标，目的就是为了计算真值和预测人体关键点的相似度。 2、OKS矩阵 OKS是