Gait Recognition Using Pose Estimation and Signal Processing

2019 ◽  
pp. 719-728 ◽  
Author(s):  
Ví­tor Cézar de Lima ◽  
William Robson Schwartz
Keyword(s):  
Signal Processing ◽  
Pose Estimation ◽  
Gait Recognition

scholarly journals 3-D Canonical Pose Estimation and Abnormal Gait Recognition With a Single RGB-D Camera

2019 ◽  
Vol 4 (4) ◽  
pp. 3617-3624 ◽  
Author(s):  
Yao Guo ◽  
Fani Deligianni ◽  
Xiao Gu ◽  
Guang-Zhong Yang
Keyword(s):  
Pose Estimation ◽  
Gait Recognition ◽  
Abnormal Gait

scholarly journals Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5466 ◽  
Author(s):  
Xinrui Jiang ◽  
Ye Zhang ◽  
Qi Yang ◽  
Bin Deng ◽  
Hongqiang Wang
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Millimeter Wave ◽  
Gait Recognition ◽  
Three Dimensional ◽  
Human Gait ◽  
Radar Signal ◽  
Learning Technology ◽  
Motion Speed ◽  
Signal Processing Algorithms

At present, there are two obvious problems in radar-based gait recognition. First, the traditional radar frequency band is difficult to meet the requirements of fine identification with due to its low carrier frequency and limited micro-Doppler resolution. Another significant problem is that radar signal processing is relatively complex, and the existing signal processing algorithms are poor in real-time usability, robustness and universality. This paper focuses on the two basic problems of human gait detection with radar and proposes a human gait classification and recognition method based on millimeter-wave array radar. Based on deep-learning technology, a multi-channel three-dimensional convolution neural network is proposed on the basis of improving the residual network, which completes the classification and recognition of human gait through the hierarchical extraction and fusion of multi-dimensional features. Taking the three-dimensional coordinates, motion speed and intensity of strong scattering points in the process of target motion as network inputs, multi-channel convolution is used to extract motion features, and the classification and recognition of typical daily actions are completed. The experimental results show that we have more than 92.5% recognition accuracy for common gait categories such as jogging and normal walking.

scholarly journals A New Method for Gait Recognition Using 2D Poses

2020 ◽  
Author(s):  
Daniel Jangua ◽  
Aparecido Marana
Keyword(s):  
Pose Estimation ◽  
Gait Recognition ◽  
Recognition Rate ◽  
New Method ◽  
Chi Square ◽  
Computational Performance ◽  
Face Features ◽  
Gait Biometrics ◽  
Biometric Systems ◽  
Important Challenge

Over the last decades, biometrics has become an important way for human identification in many areas, since it can avoid frauds and increase the security of individuals in society. Nowadays, most popular biometric systems are based on fingerprint and face features. Despite the great development observed in Biometrics, an important challenge lasts, which is the automatic people identification in low-resolution videos captured in unconstrained scenarios, at a distance, in a covert and noninvasive way, with little or none subject cooperation. In these cases, gait biometrics can be the only choice. The goal of this work is to propose a new method for gait recognition using information extracted from 2D poses estimated over video sequences. For 2D pose estimation, our method uses OpenPose, an open-source robust pose estimator, capable of real-time multi-person detection and pose estimation with high accuracy and a good computational performance. In order to assess the new proposed method, we used two public gait datasets, CASIA Gait Dataset-A and CASIA Gait Dataset-B. Both datasets have videos of a number of people walking in different directions and conditions. In our new method, the classification is carried out by a 1-NN classifier. The best results were obtained by using the chi-square distance function, which obtained 95.00% of rank-1 recognition rate on CASIA Gait Dataset-A and 94.22% of rank-1 recognition rate on CASIA Gait Dataset-B, which are comparable to state-of-the-art results.

Gait recognition: a challenging signal processing technology for biometric identification

2005 ◽  
Vol 22 (6) ◽  
pp. 78-90 ◽  
Author(s):  
N.V. Boulgouris ◽  
D. Hatzinakos ◽  
K.N. Plataniotis
Keyword(s):  
Signal Processing ◽  
Gait Recognition ◽  
Processing Technology ◽  
Biometric Identification

Improving Gait Recognition with 3D Pose Estimation

2018 ◽  
pp. 137-147 ◽  
Author(s):  
Weizhi An ◽  
Rijun Liao ◽  
Shiqi Yu ◽  
Yongzhen Huang ◽  
Pong C. Yuen
Keyword(s):  
Pose Estimation ◽  
Gait Recognition ◽  
3D Pose Estimation

Sparse Image and Signal Processing

2009 ◽  
Author(s):  
Jean-Luc Starck ◽  
Fionn Murtagh ◽  
Jalal Fadili
Keyword(s):  
Signal Processing

Signal Processing for In-Line Inspection of Gas Transmission Pipelines

1996 ◽  
Vol 8 (1) ◽  
pp. 233-247
Author(s):  
S. Mandayam ◽  
L. Udpa ◽  
S. S. Udpa ◽  
W. Lord
Keyword(s):  
Signal Processing ◽  
Transmission Pipelines
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