MTPGait: Multi-person Gait Recognition with Spatio-temporal Information via Millimeter Wave Radar

Li, Tao; Xu, Cheng; Liu, Haisong; Shi, Chenqi; Chen, Pengpeng

doi:10.1109/icpads53394.2021.00088

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…We compare the recognition accuracy of the PGGait system with other recent research works, including the solutions of mmGaitNet [ 25 ], Wu et al [ 33 ], MTPGait [ 26 ], Xia et al [ 34 ], and Pegoraro [ 35 ], and the results of the comparisons are shown in Table 1 . mmGaitNet’s research uses a dual radar technique, which shows good recognition of walks on radial paths.…”

Section: Discussionmentioning

confidence: 99%

“…This highlights the performance and limitations of the method in complex multi-person environments. In their work, Li et al [ 26 ] extracted the spatio-temporal features of a 3D point cloud concisely and efficiently using a specially designed neural network. They constructed a new millimeter-wave radar 3D point cloud gait dataset with data enhancement of the dataset.…”

Section: Related Workmentioning

confidence: 99%

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PGGait: Gait Recognition Based on Millimeter-Wave Radar Spatio-Temporal Sensing of Multidimensional Point Clouds

Dang,

Tang,

Hao

et al. 2023

Sensors

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Gait recognition, crucial in biometrics and behavioral analytics, has applications in human–computer interaction, identity verification, and health monitoring. Traditional sensors face limitations in complex or poorly lit settings. RF-based approaches, particularly millimeter-wave technology, are gaining traction for their privacy, insensitivity to light conditions, and high resolution in wireless sensing applications. In this paper, we propose a gait recognition system called Multidimensional Point Cloud Gait Recognition (PGGait). The system uses commercial millimeter-wave radar to extract high-quality point clouds through a specially designed preprocessing pipeline. This is followed by spatial clustering algorithms to separate users and perform target tracking. Simultaneously, we enhance the original point cloud data by increasing velocity and signal-to-noise ratio, forming the input of multidimensional point clouds. Finally, the system inputs the point cloud data into a neural network to extract spatial and temporal features for user identification. We implemented the PGGait system using a commercially available 77 GHz millimeter-wave radar and conducted comprehensive testing to validate its performance. Experimental results demonstrate that PGGait achieves up to 96.75% accuracy in recognizing single-user radial paths and exceeds 94.30% recognition accuracy in the two-person case. This research provides an efficient and feasible solution for user gait recognition with various applications.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

PGGait: Gait Recognition Based on Millimeter-Wave Radar Spatio-Temporal Sensing of Multidimensional Point Clouds

Dang,

Tang,

Hao

et al. 2023

Sensors

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show abstract

“…The method could achieve 90% accuracy in singleperson recognition, but the pedestrian path was a fixed path, and the method only achieved 45% accuracy in a random path. Li et al [30] proposed the MTPGait method, constructed and released a millimeter wave radar 3D point cloud data set, and designed a neural network based on CNN+LSTM to extract the multi-scale spatio-temporal characteristics of the 3D point cloud in the spatial and temporal dimensions. The results show that under the single scene with any path, MTPGait is able to achieve a recognition accuracy rate of 96.7%, and 90.2% when two people coexist.…”

Section: Gait Recognition Based On Wireless Signalmentioning

confidence: 99%

Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa

et al. 2023

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This study proposes a new approach to gait recognition using LoRa signals, taking into account the challenging conditions found in underground coal mines, such as low illumination, high temperature and humidity, high dust concentrations, and limited space. The aim is to address the limitations of existing gait recognition research, which relies on sensors or other wireless signals that are sensitive to environmental factors, costly to deploy, invasive, and require close sensing distances. The proposed method analyzes the received signal waveform and utilizes the amplitude data for gait recognition. To ensure data reliability, outlier removal and signal smoothing are performed using Hampel and S-G filters, respectively. Additionally, high-frequency noise is eliminated through the application of Butterworth filters. To enhance the discriminative power of gait features, the pre-processed data are reconstructed using an autoencoder, which effectively extracts the underlying gait behavior. The trained autoencoder generates encoder features that serve as the input matrix. The Softmax method is then employed to associate these features with individual identities, enabling LoRa-based single-target gait recognition. Experimental results demonstrate significant performance improvements. In indoor environments, the recognition accuracy for groups of 2 to 8 individuals ranges from 99.7% to 96.6%. Notably, in an underground coal mine where the target is located 20 m away from the transceiver, the recognition accuracy for eight individuals reaches 93.3%.

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Enabling Coexistence of Indoor Millimeter- Wave Networking and Human Activity Sensing

Saadat,

Sur

2024

2024 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

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MTPGait: Multi-person Gait Recognition with Spatio-temporal Information via Millimeter Wave Radar

Cited by 7 publications

References 11 publications

PGGait: Gait Recognition Based on Millimeter-Wave Radar Spatio-Temporal Sensing of Multidimensional Point Clouds

PGGait: Gait Recognition Based on Millimeter-Wave Radar Spatio-Temporal Sensing of Multidimensional Point Clouds

Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa

Enabling Coexistence of Indoor Millimeter- Wave Networking and Human Activity Sensing

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