Abstract
In this paper, we present an algorithm that computes the topological signature for a given periodic motion sequence. Such signature consists of a vector obtained by persistent homology which captures the topological and geometric changes of the object that models the motion. Two topological signatures are compared simply by the angle between the corresponding vectors. With respect to gait recognition, we have tested our method using only the lowest fourth part of the body's silhouette. In this way, the impact of variations in the upper part of the body, which are very frequent in real scenarios, decreases considerably. We have also tested our method using other periodic motions such as running or jumping. Finally, we formally prove that our method is robust to small perturbations in the input data and does not depend on the number of periods contained in the periodic motion sequence.
Abstract (translated)
本文提出了一种计算给定周期运动序列拓扑特征的算法。这种特征包含一个由持久同源性获得的向量，它捕获了运动模型对象的拓扑和几何变化。两个拓扑特征简单地通过对应向量之间的角度进行比较。关于步态识别，我们只使用了身体轮廓的最低四分之一部分来测试我们的方法。这样，在真实场景中，身体上部变化的影响会显著降低，这在真实场景中非常常见。我们还使用其他的周期性动作，如跑步或跳跃来测试我们的方法。最后，我们正式证明了该方法对输入数据中的小扰动具有鲁棒性，并且不依赖于周期运动序列中包含的周期数。
URL
https://arxiv.org/abs/1904.06210
