scholarly journals Reconstruction of 3D Human Body Pose for Gait Recognition

2005 ◽  
pp. 619-625 ◽  
Author(s):  
Hee-Deok Yang ◽  
Seong-Whan Lee
Keyword(s):  
Human Body ◽  
Gait Recognition

scholarly journals Kinect-based human gait identifcation under different covariate factors

2018 ◽  
Vol 6 (2) ◽  
pp. 1-11
Author(s):  
Azhin T. Sabir
Keyword(s):  
Human Body ◽  
Gait Recognition ◽  
Video Quality ◽  
Microsoft Kinect ◽  
Human Gait ◽  
Human Beings ◽  
Linear Discriminant ◽  
Gait Biometrics ◽  
Gait Identification ◽  
Microsoft Kinect Sensor

Introduction: Nowadays human gait identification/recognition is available in a variety of applications due to rapid advances in biometrics technology. This makes them easier to use for security and surveillance. Due to the rise in terrorist attacks during the last ten years research has focused on the biometric traits in these applications and they are now capable of recognising human beings from a distance. The main reason for my research interest in Gait biometrics is because it is unobtrusive and requires lower image/video quality compared to other biometric traits. Materials and Methods: In this paper we propose investigating Kinect-based gait recognition using non-standard gait sequences. This study examines different scenarios to highlight the challenges of non-standard gait sequences. Gait signatures are extracted from the 20 joint points of the human body using a Microsoft Kinect sensor. Results and Discussion: This feature is constructed by calculating the distances between each two joint points from the 20 joint points of the human body provided which is known as the Euclidean Distance Feature (EDF). The experiments are based on five scenarios, and a Linear Discriminant Classifier (LDC) is used to test the performance of the proposed method. Conclusions: The results of the experiments indicate that the proposed method outperforms previous work in all scenarios.

A Method of Lower Limb Joint Points Extraction Based on Pendulum Model under Arbitrary Gesture Walk

2014 ◽  
Vol 556-562 ◽  
pp. 4347-4351
Author(s):  
Ning Yang ◽  
Jin Tao Li ◽  
Rong Wang
Keyword(s):  
Human Body ◽  
Lower Limb ◽  
Extraction Method ◽  
Gait Recognition ◽  
Human Gait ◽  
Step Cycle ◽  
Joint Point ◽  
Pendulum Model ◽  
Real Time Tracking ◽  
Body Joints

The position extraction of lower limb joint points is important for gait recognition because the feature data is always based on the position of lower limb joint points. Since the detection of motion information of human body can affect the gait recognition directly, we propose a position extraction method of lower limb joint points in this paper. Through the study on the human body centroid tracking, and positioning of human lower limb joint point, we can obtain the step cycle information. It has been demonstrated via plenty experiments that the proposed method is feasible and easy for implement, since it can achieve real-time tracking and improve positioning accuracy of the human body joints, and can provide feature data for human gait recognition.

Recognition of Human Silhouette Based on Global Features

2010 ◽  
Vol 1 (4) ◽  
pp. 47-55 ◽  
Author(s):  
Milene Arantes ◽  
Adilson Gonzaga
Keyword(s):  
Human Body ◽  
Gait Recognition ◽  
Human Motion ◽  
Image Sequences ◽  
Video Sequences ◽  
Fusion Model ◽  
Global Features ◽  
Image Models ◽  
Human Joints ◽  
Human Silhouette

The aim of this paper is people recognition based on their gait. The authors propose a computer vision approach applied to video sequences extracting global features of human motion. From the skeleton, the authors extract the information about human joints. From the silhouette and the authors get the boundary features of the human body. The binary and gray-level-images contain different aspects about the human motion. This work proposes to recover the global information of the human body based on four segmented image models and applies a fusion model to improve classification. The authors consider frames as elements of distinct classes of video sequences and the sequences themselves as classes in a database. The classification rates obtained separately from four image sequences are then merged together by a fusion technique. The results were then compared with other techniques for gait recognition.

scholarly journals Robust arbitrary view gait recognition based on parametric 3D human body reconstruction and virtual posture synthesis

2016 ◽  
Vol 60 ◽  
pp. 361-377 ◽  
Author(s):  
Jian Luo ◽  
Jin Tang ◽  
Tardi Tjahjadi ◽  
Xiaoming Xiao
Keyword(s):  
Human Body ◽  
Gait Recognition

Improved Model-Free Gait Recognition Based on Human Body Part

2016 ◽  
pp. 141-161 ◽  
Author(s):  
Imad Rida ◽  
Noor Al Maadeed ◽  
Gian Luca Marcialis ◽  
Ahmed Bouridane ◽  
Romain Herault ◽  
...  
Keyword(s):  
Human Body ◽  
Gait Recognition ◽  
Body Part ◽  
Model Free ◽  
Human Body Part ◽  
Improved Model

Recognition of Human Silhouette Based on Global Features

2012 ◽  
pp. 354-363
Author(s):  
Milene Arantes ◽  
Adilson Gonzaga
Keyword(s):  
Human Body ◽  
Gait Recognition ◽  
Human Motion ◽  
Video Sequences ◽  
Global Information ◽  
Fusion Model ◽  
Global Features ◽  
Image Models ◽  
Human Joints ◽  
Human Silhouette

The aim of this paper is people recognition based on their gait. The authors propose a computer vision approach applied to video sequences extracting global features of human motion. From the skeleton, the authors extract the information about human joints. From the silhouette and the authors get the boundary features of the human body. The binary and gray-level-images contain different aspects about the human motion. This work proposes to recover the global information of the human body based on four segmented image models and applies a fusion model to improve classification. The authors consider frames as elements of distinct classes of video sequences and the sequences themselves as classes in a database. The classification rates obtained separately from four image sequences are then merged together by a fusion technique. The results were then compared with other techniques for gait recognition.

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