Human gait modeling and statistical registration for the frontal view gait data with application to the normal/abnormal gait analysis
Human gait modeling and statistical registration for the frontal view gait data with application to the normal/abnormal gait analysis
复制标题
人体步态建模和正面步态数据的统计配准，并应用于正常/异常步态分析
DOI：
10.1007/978-94-007-6818-5_37
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Kosuke Okusa and Toshinari Kamakura
中科院分区：
文献类型：
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作者：
Shuhei Inui;Kosuke Okusa;Kurato Maeno and Toshinari Kamakura;Kosuke Okusa and Toshinari Kamakura
通过对视频数据的配准和建模，研究了正面视图人体步态数据的分析和分类问题。在本研究中，我们假设正面视图步态数据是尺度变化、人体运动和速度变化参数的混合。我们的步态模型是基于人体步态结构和相机与被摄体之间的时空关系。为了验证该方法的有效性，我们进行了两组实验，分别评估了该方法在年轻人/老年人步态分析和异常步态检测中的应用。在异常步态检测实验中，我们使用K-NN分类器，使用估计的参数进行正常/异常步态检测，并给出了120名受试者（年轻人）和60名受试者（老年人）的实验结果。结果表明，该方法具有较高的检出率。
We study the problem of analyzing and classifying frontal view human gait data by registration and modeling on a video data. In this study, we suppose that frontal view gait data as a mixing of scale changing, human movements and speed changing parameter. Our gait model is based on human gait structure and temporal-spatial relations between camera and subject. To demonstrate the effectiveness of our method, we conducted two sets of experiments, assessing the proposed method in gait analysis for young/elderly person and abnormal gait detecting. In abnormal gait detecting experiment, we apply K-NN classifier, using the estimated parameters, to perform normal/abnormal gait detect, and present results from an experiment involving 120 subjects (young person), and 60 subjects (elderly person). As a result, our method shows high detection rate.