Human Activity Detection Based on Parallel AB-TCN Using Micro-Doppler Signatures

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Didi Xu;Weihua Yu;Yufeng Wang;Mengjun Chen;Yaze Cui
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引用次数: 0

Abstract

The classification and identification of human activities have been increasingly focused on in the fields of human-computer interaction, search and rescue, health detection, and so on. Deep learning methods have been widely employed in target classification recognition. To enhance the classification performance, an attention-mechanism-based two-channel network (AB-TCN) is proposed. In this architecture, the attention module is embedded into the convolutional neural network (CNN) to achieve feature enhancement and redundancy suppression in the spatial and channel domains. Furthermore, the short-window time-frequency image and long-window time-frequency image are separately input into two symmetrical channels for feature extraction and fusion to enhance the differential feature weight of target behavior. The method is simple and easy to implement, with low computational complexity. The experimental results show that the proposed method has higher detection accuracy, and the classification accuracy is increased by more than 5% compared with the traditional neural network architecture.
基于微多普勒特征并行AB-TCN的人体活动检测
人类活动的分类与识别在人机交互、搜救、健康检测等领域受到越来越多的关注。深度学习方法在目标分类识别中得到了广泛的应用。为了提高分类性能,提出了一种基于注意机制的双通道网络(AB-TCN)。在该架构中,注意力模块被嵌入到卷积神经网络(CNN)中,以实现空间域和信道域的特征增强和冗余抑制。此外,将短窗时频图像和长窗时频图像分别输入到两个对称通道中进行特征提取和融合,增强目标行为的差分特征权重。该方法简单易行,计算复杂度低。实验结果表明,该方法具有较高的检测精度,与传统神经网络结构相比,分类精度提高了5%以上。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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