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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Unsupervised Domain Adaptation for Micro-Doppler Human Motion Classification via Feature Fusion
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Unsupervised Domain Adaptation for Micro-Doppler Human Motion Classification via Feature Fusion

机译：通过特征融合对微多普勒人体运动分类的无监督域自适应

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摘要

Micro-Doppler-based human motion classification has become a topical area of research recently. However, the current research is limited by the lack of labeled training data. Domain adaptation, namely, the ability to take advantage of knowledge from an available source data set and apply it to an unlabeled target data set, is useful in this situation. A typical strategy for this transfer learning technique is to extract domain-invariant feature representations. In this letter, an unsupervised domain adaptation method for micro-Doppler classification is proposed. Given no available measurement training samples, we creatively utilize the motion capture database as an auxiliary and adapt its interior knowledge to the measurement data set. To achieve domain-invariant features, three types of features are extracted and fused including low-level deep features from the convolutional neural network, empirical features, and statistical features. After feature fusion, a k-nearest neighbor classifier is applied to the measurement data to classify seven human activities. Experimental results show that our approach outperforms several state-of-the-art unsupervised domain adaptation methods. The impact of the output from different convolution layers is further investigated, and ablation studies of the efficacy of each feature are also carried out in this letter.
机译：基于微多普勒的人体运动分类近来已成为研究的热点领域。但是，当前的研究受到缺乏标记训练数据的限制。在这种情况下，领域适应（即利用来自可用源数据集的知识并将其应用于未标记目标数据集的能力）非常有用。这种转移学习技术的典型策略是提取领域不变特征表示。在这封信中，提出了一种用于微多普勒分类的无监督域自适应方法。在没有可用的测量训练样本的情况下，我们创造性地利用运动捕捉数据库作为辅助，并将其内部知识适应于测量数据集。为了实现领域不变特征，提取并融合了三种类型的特征，包括来自卷积神经网络的低层深度特征，经验特征和统计特征。特征融合后，将k最近邻分类器应用于测量数据以对七种人类活动进行分类。实验结果表明，我们的方法优于几种最新的无监督域自适应方法。进一步研究了来自不同卷积层的输出的影响，并且在这封信中还对每个功能的功效进行了消融研究。

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