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

ZHAO Yaqin; SONG Yuqing; WU Han; HE Shengyang; LIU Puqiu; WU Longwen

doi:10.11999/JEIT230165

Volume 46 Issue 3

Mar. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(3): 967-976

ZHAO Yaqin, SONG Yuqing, WU Han, HE Shengyang, LIU Puqiu, WU Longwen. High-precision Gesture Recognition Based on DenseNet and Convolutional Block Attention Module[J]. Journal of Electronics & Information Technology, 2024, 46(3): 967-976. doi: 10.11999/JEIT230165

Citation:

ZHAO Yaqin, SONG Yuqing, WU Han, HE Shengyang, LIU Puqiu, WU Longwen. High-precision Gesture Recognition Based on DenseNet and Convolutional Block Attention Module[J]. Journal of Electronics & Information Technology, 2024, 46(3): 967-976. doi: 10.11999/JEIT230165

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

doi: 10.11999/JEIT230165 cstr: 32379.14.JEIT230165

1.
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
2.
China Aerospace Science & Industry Corporation 8511 company, Nanjing 211100, China

Funds: The National Natural Science Foundation of China (61671185, 62071153)

Received Date: 2023-03-16
Rev Recd Date: 2023-09-07

Available Online: 2023-09-12

Publish Date: 2024-03-27

Abstract

Abstract

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.
- Gesture recognition,
- Millimeter wave radar,
- Convolutional Neural Network(CNN),
- Convolutional Block Attention Module(CBAM)

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References(30)

References

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