Occlusion is an important factor for analysis of human gait recognition in real-time scenarios. In multi-person gait (MPG) or dynamic occlusion, gait recognition is affected due to occluded body parts known as region of interests (ROIs). The aim of this article is to reconstruct the occluded ROIs and measure the errors associated with the reconstruction methods. The contribution of this article is threefold: firstly, we segment five dynamic ROIs; secondly, reconstruction of ROIs using Lagrange, piecewise cubic hermite (PCH) and cubic spline and thirdly, a comparison among the above methods in MPG scenario. We consider the human body into two parts, i.e., lower and upper body. In lower body, we have considered ankle, while knee in upper body: wrist, elbow, and shoulder have been considered. The dataset used in this study consists of dynamic occlusion scenarios. The quantitative assessment of the above methods are based on four parameters such as mean square error, root mean square error, mean absolute error and mean absolute percentage error. Results show that PCH consistently outperforms the other methods in the reconstruction of occluded ROIs in MPG scenario.

中文翻译：

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基于数值方法的多人步态遮挡ROI重建

遮挡是分析实时场景中人体步态识别的重要因素。在多人步态 (MPG) 或动态遮挡中，步态识别会由于被称为感兴趣区域 (ROI) 的被遮挡的身体部位而受到影响。本文的目的是重建被遮挡的 ROI 并测量与重建方法相关的误差。本文的贡献有三点：首先，我们对五个动态 ROI 进行细分；其次，使用拉格朗日、分段三次厄米 (PCH) 和三次样条重建 ROI，第三，在 MPG 场景中比较上述方法。我们将人体分为两部分，即下半身和上半身。在下半身，我们考虑了脚踝，而在上半身：手腕、肘部和肩部，我们考虑了膝盖。本研究中使用的数据集由动态遮挡场景组成。上述方法的定量评估基于均方误差、均方根误差、平均绝对误差和平均绝对百分比误差四个参数。结果表明，在 MPG 场景中，PCH 在重建被遮挡的 ROI 方面始终优于其他方法。