Event-Driven Gait Recognition Method Based on Dynamic Temporal Segmentation

2015 ◽  
Author(s):  
Xiaochen Lai ◽  
Guoqiao Zhou ◽  
Chi Lin ◽  
Kangbin Yim
Keyword(s):  
Gait Recognition ◽  
Temporal Segmentation ◽  
Recognition Method ◽  
Event Driven

scholarly journals Skeleton based gait recognition for long and baggy clothes

2019 ◽  
Vol 277 ◽  
pp. 03005
Author(s):  
Abrar Alharbi ◽  
Fahad Alharbi ◽  
Eiji Kamioka
Keyword(s):  
Gait Recognition ◽  
User Interaction ◽  
Microsoft Kinect ◽  
Human Gait ◽  
Kinect Sensor ◽  
Recognition Method ◽  
Walking Gait ◽  
Biometric Feature ◽  
Gait Database ◽  
Microsoft Kinect Sensor

Human gait is a significant biometric feature used for the identification of people by their style of walking. Gait offers recognition from a distance at low resolution while requiring no user interaction. On the other hand, other biometrics are likely to require a certain level of interaction. In this paper, a human gait recognition method is presented to identify people who are wearing long baggy clothes like Thobe and Abaya. Microsoft Kinect sensor is used as a tool to establish a skeleton based gait database. The skeleton joint positions are obtained and used to create five different datasets. Each dataset contained different combination of joints to explore their effectiveness. An evaluation experiment was carried out with 20 walking subjects, each having 25 walking sequences in total. The results achieved good recognition rates up to 97%.

Depth-Based Real-Time Gait Recognition

2020 ◽  
Vol 29 (16) ◽  
pp. 2050266
Author(s):  
Adnan Ramakić ◽  
Diego Sušanj ◽  
Kristijan Lenac ◽  
Zlatko Bundalo
Keyword(s):  
Real Time ◽  
Gait Recognition ◽  
Video Stream ◽  
Outdoor Environment ◽  
Microsoft Kinect ◽  
Recognition Method ◽  
Real Time Processing ◽  
Depth Data ◽  
Video Images ◽  
Subject Identification

Each person describes unique patterns during gait cycles and this information can be extracted from live video stream and used for subject identification. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. In this paper, a method to enhance the appearance-based gait recognition method by also integrating features extracted from depth data is proposed. Two approaches are proposed that integrate simple depth features in a way suitable for real-time processing. Unlike previously presented works which usually use a short range sensors like Microsoft Kinect, here, a long-range stereo camera in outdoor environment is used. The experimental results for the proposed approaches show that recognition rates are improved when compared to existing popular gait recognition methods.

Gait Recognition Based on Locality Preserving Projections

2015 ◽  
Vol 738-739 ◽  
pp. 625-630
Author(s):  
Chao Li ◽  
Jin Ye Peng ◽  
Jing Guo ◽  
Xian Feng Wang ◽  
Xu Qi Wang
Keyword(s):  
Nearest Neighbor ◽  
Wavelet Packet ◽  
Gait Recognition ◽  
Recognition Method ◽  
The Public ◽  
Nearest Neighbor Classifier ◽  
Locality Preserving Projections ◽  
Locality Preserving ◽  
Neighbor Classifier

A gait recognition method based on wavelet packet decomposition (WPD) and Locality preserving projections (LPP) is proposed in this paper. The method includes the following steps, pretreatment, feature extraction by WPD and dimensionality reduction by LPP and classification of the test samples to a corresponding class according to the nearest neighbor classifier. The experiment results on the public gait database show the effectiveness of the proposed method.

Cross-View Gait Recognition in 3-D Space Based on Gravity Center Track

2014 ◽  
Vol 644-650 ◽  
pp. 4210-4215
Author(s):  
Xin Chen ◽  
Tian Qi Yang
Keyword(s):  
Gait Recognition ◽  
Least Square Method ◽  
Least Square ◽  
Cluster Method ◽  
Direction Vector ◽  
Recognition Method ◽  
Body Area ◽  
Gravity Center ◽  
Center Track ◽  
Gait Database

We propose in this paper a novel cross-view gait recognition method based on gravity center trajectory (GCT). Inspired by the finding that if the GCT of human in walking process has regularity, the representation coefficients of the trajectory are generally consistent across different views. We propose to project the coefficients of GCT to different view plane (VP) which is the normal plane of view angle direction vector to achieve view-invariant features for gait recognition. Firstly, we obtain the GCT under different views by summation of pixel coordinates in body area. Then, we use the least square method to eliminate the upward or downward trend of GCT caused by view variance. Then, we project the GCT function to the corresponding VP. Lastly, we perform recognition by using a simple cluster method. Experimental results on the widely used CASIA-B gait database demonstrate the effectiveness and practicability of the proposed method.

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