The aim of this work is to develop a common automatic computer method to distinguish human individuals with abnormal gait patterns from those with normal gait patterns. As long as the silhouette gait images of the subjects are obtainable, the proposed method is capable of providing online anomaly gait detection result without additional work on analyzing the gait features of the target subjects before ahead. Moreover, the proposed method does not need any parameter settings by users and can start producing detection results under the work by only collecting a very small number of gait samples, even though none of those gait samples are abnormal. Therefore, the proposed method can provide fast and simple deployment for various anomaly gait detection application scenarios. The proposed method is composed of two main modules: (1) feature extraction from gait images and (2) anomaly detection via binary classification. In the first module, a new representation of the most frequently involved area of the silhouette gait images called full gait energy image (F-GEI) is proposed. Furthermore, based on the F-GEI, a novel and simple method characterizing individual walking properties is developed to extract gait features from individual subjects. In the second module, based on the very limited prior knowledge on the target dataset, a semisupervised clustering algorithm is proposed to perform the binary classification for detecting the gait anomaly of each subject. The performance of the proposed gait anomaly detection method was evaluated on the human gaits dataset in comparison with three state-of-the-art methods. The experiment results show that the proposed method is an effective and efficient gait anomaly detection method in terms of accuracy, robustness, and computational efficiency.

中文翻译：

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一种基于半监督聚类的高效步态异常自动检测方法

这项工作的目的是开发一种通用的自动计算机方法，以区分步态异常的人和步态正常的人。只要能够获得对象的步态步态图像，所提出的方法就能够提供在线的异常步态检测结果，而无需在进行之前就额外地分析目标对象的步态特征。此外，所提出的方法不需要用户进行任何参数设置，并且即使这些步态样本都没有异常，也可以通过仅收集非常少量的步态样本来开始产生检测结果。因此，所提出的方法可以为各种异常步态检测应用场景提供快速简便的部署。所提出的方法由两个主要模块组成：（1）从步态图像中提取特征，以及（2）通过二元分类进行异常检测。在第一个模块中，提出了剪影步态图像最频繁涉及的区域的新表示形式，称为全步态能量图像（F-GEI）。此外，基于F-GEI，开发了一种新颖且简单的表征个体步行特性的方法，以从个体受试者中提取步态特征。在第二个模块中，基于对目标数据集的非常有限的先验知识，提出了一种半监督聚类算法来执行二进制分类，以检测每个对象的步态异常。与三种最新方法相比，在人的步态数据集上评估了所提出的步态异常检测方法的性能。