GSI: efficient spatio-temporal template for human gait recognition

2018 ◽  
Vol 10 (1) ◽  
pp. 29 ◽  
Author(s):  
Mohammad H. Ghaeminia ◽  
Shahriar B. Shokouhi
Keyword(s):  
Gait Recognition ◽  
Human Gait ◽  
Spatio Temporal

GSI: efficient spatio-temporal template for human gait recognition

2018 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Shahriar B. Shokouhi ◽  
Mohammad H. Ghaeminia
Keyword(s):  
Gait Recognition ◽  
Human Gait ◽  
Spatio Temporal

scholarly journals Human gait identification using Kinect sensor

2017 ◽  
Vol 2 (3) ◽  
pp. 142-146 ◽  
Author(s):  
Azhin Tahir Sabir ◽  
Mohammed H. Ahmed ◽  
Abdulbasit K. Faeq ◽  
Halgurd S. Maghdid
Keyword(s):  
Gait Recognition ◽  
Temporal Variations ◽  
Human Gait ◽  
Three Dimension ◽  
Kinect Sensor ◽  
Dynamic Features ◽  
Linear Discriminant ◽  
Measured Distance ◽  
Spatio Temporal ◽  
Gait Identification

This study investigates a novel three-dimension gait recognition approach based on skeleton representation of motion by the cheap consumer level camera Kinect sensor. In this work, a new exemplification of human gait signature is proposed using the spatio-temporal variations in relative angles among various skeletal joints and changing of measured distance between limbs and land. These measurements are computed during one gait cycle. Further, we have created our own dataset based on Kinect sensor and extract two sets of dynamic features. Nearest Neighbors and Linear Discriminant Classifier (LDC) are used for classification. The results of the experiments show the proposed approach as an effective and human gait recognizer in comparison with current Kinect-based gait recognition methods.

Human gait recognition by the fusion of motion and static spatio-temporal templates

2007 ◽  
Vol 40 (9) ◽  
pp. 2563-2573 ◽  
Author(s):  
Toby H.W. Lam ◽  
Raymond S.T. Lee ◽  
David Zhang
Keyword(s):  
Gait Recognition ◽  
Human Gait ◽  
Spatio Temporal

Video-based Indoor Human Gait Recognition Using Depth Imaging and Hidden Markov Model: A Smart System for Smart Home

2010 ◽  
Vol 20 (1) ◽  
pp. 120-128 ◽  
Author(s):  
Md. Zia Uddin ◽  
Tae-Seong Kim ◽  
Jeong Tai Kim
Keyword(s):  
Hidden Markov ◽  
Gait Recognition ◽  
Smart Homes ◽  
The Elderly ◽  
Recognition System ◽  
Home Healthcare ◽  
Human Gait ◽  
Depth Imaging ◽  
Smart System ◽  
Spatiotemporal Features

Smart homes that are capable of home healthcare and e-Health services are receiving much attention due to their potential for better care of the elderly and disabled in an indoor environment. Recently the Center for Sustainable Healthy Buildings at Kyung Hee University has developed a novel indoor human activity recognition methodology based on depth imaging of a user’s activities. This system utilizes Independent Component Analysis to extract spatiotemporal features from a series of depth silhouettes of various activities. To recognise the activities from the spatiotemporal features, trained Hidden Markov Models of the activities would be used. In this study, this technique has been extended to recognise human gaits (including normal and abnormal). Since this system could be of great significance for the caring of the elderly, to promote and preserve their health and independence, the gait recognition system would be considered a primary function of the smart system for smart homes. The indoor gait recognition system is trained to detect abnormal gait patterns and generate warnings. The system works in real-time and is aimed to be installed at smart homes. This paper provides the information for further development of the system for their application in the future.

GaitSlice: A gait recognition model based on spatio-temporal slice features

2021 ◽  
pp. 108453
Author(s):  
Huakang Li ◽  
Yidan Qiu ◽  
Huimin Zhao ◽  
Jin Zhan ◽  
Rongjun Chen ◽  
...  
Keyword(s):  
Gait Recognition ◽  
Recognition Model ◽  
Model Based ◽  
Spatio Temporal
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