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.

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.

scholarly journals Acceleration Feature Extraction of Human Body Based on Wearable Devices

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 924
Author(s):  
Zhenzhen Huang ◽  
Qiang Niu ◽  
Ilsun You ◽  
Giovanni Pau
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Kalman Filter ◽  
Human Body ◽  
Wearable Devices ◽  
Human Motion ◽  
Data Set ◽  
Feature Extraction Method ◽  
Motion Data ◽  
Kalman Filter Algorithm

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

scholarly journals Elastic Simulation of Joints with Particle-Based Fluid

2021 ◽  
Vol 11 (15) ◽  
pp. 6900
Author(s):  
Su-Kyung Sung ◽  
Sang-Won Han ◽  
Byeong-Seok Shin
Keyword(s):  
Principal Stress ◽  
Human Body ◽  
Yield Condition ◽  
Maximum Principal Stress ◽  
Human Motion ◽  
The Difference ◽  
The Moment ◽  
External Forces ◽  
Physical Changes ◽  
Elastic Simulation

Skinning, which is used in skeletal simulations to express the human body, has been weighted between bones to enable muscle-like motions. Weighting is not a form of calculating the pressure and density of muscle fibers in the human body. Therefore, it is not possible to express physical changes when external forces are applied. To express a similar behavior, an animator arbitrarily customizes the weight values. In this study, we apply the kernel and pressure-dependent density variations used in particle-based fluid simulations to skinning simulations. As a result, surface tension and elasticity between particles are applied to muscles, indicating realistic human motion. We also propose a tension yield condition that reflects Tresca’s yield condition, which can be easily approximated using the difference between the maximum and minimum values of the principal stress to simulate the tension limit of the muscle fiber. The density received by particles in the kernel is assumed to be the principal stress. The difference is calculated by approximating the moment of greatest force to the maximum principal stress and the moment of least force to the minimum principal stress. When the density of a particle increases beyond the yield condition, the object is no longer subjected to force. As a result, one can express realistic muscles.

Video-Based Human Motion Analysis

2011 ◽  
Vol 403-408 ◽  
pp. 2593-2597
Author(s):  
Hong Bao ◽  
Zhi Min Liu
Keyword(s):  
Motion Analysis ◽  
Human Body ◽  
Body Shape ◽  
Human Motion ◽  
Random Motion ◽  
Human Motion Analysis ◽  
Mass Center ◽  
Shape Features ◽  
Skeleton Model ◽  
Regular Motion

In the analysis of human motion, movement was divided into regular motion (such as walking and running) and random motion (such as falling down).Human skeleton model is used in this paper to do the video-based analysis. Key joints on human body were chosen to be traced instead of tracking the entire human body. Shape features like mass center trajectory were used to describe the movement, and to classify human motion. desired results achieved.

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.

scholarly journals Sonifying the Shape of Human Body Motion using Motiongrams

2013 ◽  
Vol 8 (2) ◽  
pp. 73 ◽  
Author(s):  
Alexander Refsum Jensenius ◽  
Rolf Inge Godøy
Keyword(s):  
Human Body ◽  
Video Recording ◽  
Visual Display ◽  
Human Motion ◽  
Visual Similarity ◽  
Body Motion ◽  
Auditory Displays ◽  
Musical Sound ◽  
Starting Point ◽  
Spatial Shape

<p class="author">The paper presents sonomotiongram, a technique for the creation of auditory displays of human body motion based on motiongrams. A motiongram is a visual display of motion, based on frame differencing and reduction of a regular video recording. The resultant motiongram shows the spatial shape of the motion as it unfolds in time, somewhat similar to the way in which spectrograms visualise the shape of (musical) sound. The visual similarity of motiongrams and spectrograms is the conceptual starting point for the sonomotiongram technique, which explores how motiongrams can be turned into sound using &ldquo;inverse FFT&rdquo;. The paper presents the idea of shape-sonification, gives an overview of the sonomotiongram technique, and discusses sonification examples of both simple and complex human motion.</p>

Biomechanics of human walking and stability descriptive parameters

2016 ◽  
Vol 2 (1) ◽  
pp. 4
Author(s):  
Arturo Bertomeu-Motos
Keyword(s):  
Human Body ◽  
Center Of Pressure ◽  
Body Movement ◽  
Human Motion ◽  
Human Walking ◽  
Dynamic Motion ◽  
Mechanical Aspect ◽  
Zero Moment

From the time of Aristotle onward, there have been countless books written on the topic of movement in animals and humans. However, research of human motion, especially walking mechanisms, has increased over the last fifty years. The study of human body movement and its stability during locomotion involves both neuronal and mechanical aspect. The mechanical aspect, which is in the scope of this thesis, requires knowledge in the field of biomechanics. Walking is the most common maneuver of displacement for humans and it is performed by a stable dynamic motion. In this article it is introduced the bases of the human walking in biomechanical terms. Furthermore, two stability descriptive parameters during walking are also explained - Center of Pressure (CoP) and Zero-Moment Pint (ZMP).

Sign in / Sign up

Export Citation Format

Share Document