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arXiv 2410.02771cs.CVcs.AI

基于雷达图像的手势复值卷积神经网络分类

Complex-valued convolutional neural network classification of hand gesture from radar images

University of Manchester（曼彻斯特大学）

Shokooh Khandan

备注：
173 pages, 36 tables, 50 figures
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AI总结：

针对现有手势识别算法多基于实值、处理复值雷达图像时维度翻倍的问题，提出全复值CNN及复值前向残差网络，在复值手势雷达数据集上验证了性能。

摘要：

过去十年间，手势识别系统取得了诸多令人瞩目的进展，在人机交互（HCI）领域的应用愈发广泛，覆盖安全防护、安防以及汽车等多个应用领域。研究人员已针对手势识别系统探索了多种深度神经网络架构，包括多层感知器（MLP）、卷积神经网络（CNN）、循环神经网络（RNN），以及由后两种架构级联而成的CNN-RNN。然而，目前仍存在一个核心问题：现有大多数机器学习算法的构建模块与技术都是针对实值（RV）设计开发的。研究人员将多种实值技术应用于复值（CV）雷达图像，例如通过将复数拆分为实部和虚部，把复值优化问题转化为实值优化问题。但该方法的主要缺陷在于，最终得到的算法会使网络维度翻倍。近期关于循环神经网络的研究及其他基础理论分析表明，复数具有更强的表征能力，但由于缺乏设计此类模型所需的构建模块，复值网络的性能受到了限制。本报告提出了一种全复值卷积神经网络（CV-CNN），其所有构建模块、前向与反向运算以及导数均在复数域内定义。我们在两组复值手势雷达图像上对所提出的分类模型进行了探究，并与等效的实值模型进行了对比。在第五章中，我们提出了一种复值前向残差网络，用于对两组复值手势雷达数据集进行二分类，并将其性能与我们提出的CV-CNN以及基线复值前向CNN进行了对比。

英文摘要：

Hand gesture recognition systems have yielded many exciting advancements in the last decade and become more popular in HCI (human-computer interaction) with several application areas, which spans from safety and security applications to automotive field. Various deep neural network architectures have already been inspected for hand gesture recognition systems, including multi-layer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN) and a cascade of the last two architectures known as CNN-RNN. However, a major problem still exists, which is most of the existing ML algorithms are designed and developed the building blocks and techniques for real-valued (RV). Researchers applied various RV techniques on the complex-valued (CV) radar images, such as converting a CV optimisation problem into a RV one, by splitting the complex numbers into their real and imaginary parts. However, the major disadvantage of this method is that the resulting algorithm will double the network dimensions. Recent work on RNNs and other fundamental theoretical analysis suggest that CV numbers have a richer representational capacity, but due to the absence of the building blocks required to design such models, the performance of CV networks are marginalised. In this report, we propose a fully CV-CNN, including all building blocks, forward and backward operations, and derivatives all in complex domain. We explore our proposed classification model on two sets of CV hand gesture radar images in comparison with the equivalent RV model. In chapter five, we propose a CV-forward residual network, for the purpose of binary classification of the two sets of CV hand gesture radar datasets and compare its performance with our proposed CV-CNN and a baseline CV-forward CNN.

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