Gait Recognition Using Optical Motion Capture: A Decision Fusion Based Method.
Gait Recognition Using Optical Motion Capture: A Decision Fusion Based Method.
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DOI：
10.3390/s21103496
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发表时间：
2021-05-17
期刊：
影响因子：
--
通讯作者：
Zhang W
中科院分区：
文献类型：
--
作者：
Wang L;Li Y;Xiong F;Zhang W
基于运动捕捉数据的身份识别技术因其在身份认证和监控系统中的广泛应用而受到广泛关注。光学运动捕捉系统（OMCS）可以动态地捕捉人体上光学跟踪器的高精度三维位置，但其在步态识别方面的应用潜力尚未得到研究。另一方面，典型的OMCS一次只能支持一个播放器，这限制了它的能力和效率。本文的目标是研究基于OMCS的步态识别性能，并在OMCS中实现步态识别，使其能够同时支持多个玩家。提出了一种基于决策融合的步态识别方法，包括特征提取、不可靠特征标定、单运动帧分类和多运动帧决策融合四个步骤。我们使用核极端学习机（KELM）的单一运动分类，特别是我们提出了一个可靠性加权和（RWS）决策融合方法，联合收割机的运动帧的模糊决策。实验结果表明，KELM算法在单帧运动分类任务中的性能明显优于支持向量机（SVM）和随机森林算法，并证明了所提出的RWS决策融合规则与传统融合规则相比具有更高的融合精度.我们的研究结果还表明，与10个运动跟踪器上实施的下半身位置，所提出的方法可以实现100%的验证精度与少于50个步态运动帧。
Human identification based on motion capture data has received signification attentions for its wide applications in authentication and surveillance systems. The optical motion capture system (OMCS) can dynamically capture the high-precision three-dimensional locations of optical trackers that are implemented on a human body, but its potential in applications on gait recognition has not been studied in existing works. On the other hand, a typical OMCS can only support one player one time, which limits its capability and efficiency. In this paper, our goals are investigating the performance of OMCS-based gait recognition performance, and realizing gait recognition in OMCS such that it can support multiple players at the same time. We develop a gait recognition method based on decision fusion, and it includes the following four steps: feature extraction, unreliable feature calibration, classification of single motion frame, and decision fusion of multiple motion frame. We use kernel extreme learning machine (KELM) for single motion classification, and in particular we propose a reliability weighted sum (RWS) decision fusion method to combine the fuzzy decisions of the motion frames. We demonstrate the performance of the proposed method by using walking gait data collected from 76 participants, and results show that KELM significantly outperforms support vector machine (SVM) and random forest in the single motion frame classification task, and demonstrate that the proposed RWS decision fusion rule can achieve better fusion accuracy compared with conventional fusion rules. Our results also show that, with 10 motion trackers that are implemented on lower body locations, the proposed method can achieve 100% validation accuracy with less than 50 gait motion frames.
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