High-precision Gesture Recognition Based on DenseNet and Convolutional Block Attention Module

被引：0
|
作者：
Zhao Y. [1 ]
Song Y. [1 ]
Wu H. [1 ]
He S. [1 ]
Liu P. [2 ]
Wu L. [1 ]
机构：
[1] School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin
[2] China Aerospace Science & Industry Corporation, 8511 company, Nanjing
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2024年 / 46卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Convolutional Block Attention Module(CBAM); Convolutional Neural Network(CNN); Gesture recognition; Millimeter wave radar;
D O I：
10.11999/JEIT230165
中图分类号：
学科分类号：
摘要：
Non-contact gesture recognition is a new type of human-computer interaction method with broad application prospects. It can be used in Augmented Reality (AR)/Virtual Reality (VR), smart homes, smart medical, etc., and has recently become a research hotspot. Motivated by the need for more precise micro-motion gesture recognition using mm-wave radar in recent years, a novel micro-motion gesture recognition method based on MIMO millimeter wave radar is proposed in this paper. The MilliMeter Wave CAScaded (MMWCAS) radar cascaded with four AWR1243 radar boards is used to collect gesture echoes. Time-frequency analysis is performed on gesture echoes, and the hand target is detected based on the Range-Doppler (RD) map and 3D point cloud. Through data pre-processing, the Range-Time Map (RTM), Doppler-Time Map (DTM), Azimuth-Time Map (ATM) and Elevation-Time Map (ETM) of the gestures are extracted to more comprehensively characterize the motion of the hand gesture. The mixed Feature-Time Maps (FTM) are formed and adopted for the recognition of 12 types of micro-motion gestures. An innovative gesture recognition network based on DenseNet and Convolutional Block Attention Module (CBAM) is designed, and the mixed FTM is used as the input of the network. Experimental results show that the recognition accuracy reaches 99.03%, achieving high-accuracy gesture recognition. It is discovered that the network focuses on the first half of the gesture movement, which improves the recognition accuracy. © 2024 Science Press. All rights reserved.
引用
收藏
页码：967 / 976
页数：9
相关论文
共 30 条
  • [11] ZHANG Xuhao, WU Qisong, ZHAO Dixian, Dynamic hand gesture recognition using FMCW radar sensor for driving assistance[C], 2018 10th International Conference on Wireless Communications and Signal Processing, pp. 1-6, (2018)
  • [12] YU J T, YEN L, TSENG P H., mmWave radar-based hand gesture recognition using range-angle image[C], 2020 IEEE 91st Vehicular Technology Conference, pp. 1-5, (2020)
  • [13] LIU Haipeng, ZHOU Anfu, DONG Zihe, Et al., M-Gesture: Person-independent real-time in-air gesture recognition using commodity millimeter wave radar[J], IEEE Internet of Things Journal, 9, 5, pp. 3397-3415, (2022)
  • [14] SMITH J W, THIAGARAJAN S, WILLIS R, Et al., Improved static hand gesture classification on deep convolutional neural networks using novel sterile training technique[J], IEEE Access, 9, pp. 10893-10902, (2021)
  • [15] GAN Liangyu, LIU Yuan, LI Yanzhong, Et al., Gesture recognition system using 24 GHz FMCW radar sensor realized on real-time edge computing platform[J], IEEE Sensors Journal, 22, 9, pp. 8904-8914, (2022)
  • [16] WANG Yong, WU Jinjun, TIAN Zengshan, Et al., Gesture recognition with multi-dimensional parameter using FMCW radar[J], Journal of Electronics & Information Technology, 41, 4, pp. 822-829, (2019)
  • [17] WANG Yong, WANG Shasha, TIAN Zengshan, Et al., Two-stream fusion neural network approach for hand gesture recognition based on FMCW radar[J], Acta Electronica Sinica, 47, 7, pp. 1408-1415, (2019)
  • [18] AHMED S, KIM W, PARK J, Et al., Radar-based air-writing gesture recognition using a novel multistream CNN approach[J], IEEE Internet of Things Journal, 9, 23, pp. 23869-23880, (2022)
  • [19] ALIREZAZAD K, MAURER L., FMCW radar-based hand gesture recognition using dual-stream CNN-GRU model[C], 2022 24th International Microwave and Radar Conference, pp. 1-5, (2022)
  • [20] PARK G, CHANDRASEGAR V K, PARK J, Et al., Increasing accuracy of hand gesture recognition using convolutional neural network[C], 2022 International Conference on Artificial Intelligence in Information and Communication, pp. 251-255, (2022)
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