11,012 research outputs found
Gait recognition based on shape and motion analysis of silhouette contours
This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject’s silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subject’s shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subject’s back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subject’s leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods
On gait as a biometric: progress and prospects
There is increasing interest in automatic recognition by gait given its unique capability to recognize people at a distance when other biometrics are obscured. Application domains are those of any noninvasive biometric, but with particular advantage in surveillance scenarios. Its recognition capability is supported by studies in other domains such as medicine (biomechanics), mathematics and psychology which also suggest that gait is unique. Further, examples of recognition by gait can be found in literature, with early reference by Shakespeare concerning recognition by the way people walk. Many of the current approaches confirm the early results that suggested gait could be used for identification, and now on much larger databases. This has been especially influenced by DARPA’s Human ID at a Distance research program with its wide scenario of data and approaches. Gait has benefited from the developments in other biometrics and has led to new insight particularly in view of covariates. Equally, gait-recognition approaches concern extraction and description of moving articulated shapes and this has wider implications than just in biometrics
Covariate conscious approach for Gait recognition based upon Zernike moment invariants
Gait recognition i.e. identification of an individual from his/her walking
pattern is an emerging field. While existing gait recognition techniques
perform satisfactorily in normal walking conditions, there performance tend to
suffer drastically with variations in clothing and carrying conditions. In this
work, we propose a novel covariate cognizant framework to deal with the
presence of such covariates. We describe gait motion by forming a single 2D
spatio-temporal template from video sequence, called Average Energy Silhouette
image (AESI). Zernike moment invariants (ZMIs) are then computed to screen the
parts of AESI infected with covariates. Following this, features are extracted
from Spatial Distribution of Oriented Gradients (SDOGs) and novel Mean of
Directional Pixels (MDPs) methods. The obtained features are fused together to
form the final well-endowed feature set. Experimental evaluation of the
proposed framework on three publicly available datasets i.e. CASIA dataset B,
OU-ISIR Treadmill dataset B and USF Human-ID challenge dataset with recently
published gait recognition approaches, prove its superior performance.Comment: 11 page
Gait Recognition from Motion Capture Data
Gait recognition from motion capture data, as a pattern classification
discipline, can be improved by the use of machine learning. This paper
contributes to the state-of-the-art with a statistical approach for extracting
robust gait features directly from raw data by a modification of Linear
Discriminant Analysis with Maximum Margin Criterion. Experiments on the CMU
MoCap database show that the suggested method outperforms thirteen relevant
methods based on geometric features and a method to learn the features by a
combination of Principal Component Analysis and Linear Discriminant Analysis.
The methods are evaluated in terms of the distribution of biometric templates
in respective feature spaces expressed in a number of class separability
coefficients and classification metrics. Results also indicate a high
portability of learned features, that means, we can learn what aspects of walk
people generally differ in and extract those as general gait features.
Recognizing people without needing group-specific features is convenient as
particular people might not always provide annotated learning data. As a
contribution to reproducible research, our evaluation framework and database
have been made publicly available. This research makes motion capture
technology directly applicable for human recognition.Comment: Preprint. Full paper accepted at the ACM Transactions on Multimedia
Computing, Communications, and Applications (TOMM), special issue on
Representation, Analysis and Recognition of 3D Humans. 18 pages. arXiv admin
note: substantial text overlap with arXiv:1701.00995, arXiv:1609.04392,
arXiv:1609.0693
A Review of Chinese Academy of Sciences (CASIA) Gait Database As a Human Gait Recognition Dataset
Human Gait as the recognition object is the famous
biometrics system recently. Many researchers had
focused this subject to consider for a new recognition
system. One of the important advantage in this
recognition compare to other is it does not require
observed subject’s attention and cooperation. There are
many human gait datasets created within the last 10
years. Some databases that are widely used are
University Of South Florida (USF) Gait Dataset,
Chinese Academy of Sciences (CASIA) Gait Dataset, and
Southampton University (SOTON) Gait Dataset. This
paper will analyze the CASIA Gait Dataset in order to see their characteristics. There are 2 pre-processing subsystems; model based and model free approach. We will use 2D Discrete Wavelet Transform (DWT). We select Haar wavelets to reduce and extract the feature
Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors
This paper presents a gait recognition method which combines spatio-temporal motion characteristics, statistical and physical parameters (referred to as STM-SPP) of a human subject for its classification by analysing shape of the subject's silhouette contours using Procrustes shape analysis (PSA) and elliptic Fourier descriptors (EFDs). STM-SPP uses spatio-temporal gait characteristics and physical parameters of human body to resolve similar dissimilarity scores between probe and gallery sequences obtained by PSA. A part-based shape analysis using EFDs is also introduced to achieve robustness against carrying conditions. The classification results by PSA and EFDs are combined, resolving tie in ranking using contour matching based on Hu moments. Experimental results show STM-SPP outperforms several silhouette-based gait recognition methods
Design of an Elastic Actuation System for a Gait-Assistive Active Orthosis for Incomplete Spinal Cord Injured Subjects
A spinal cord injury severely reduces the quality of life of affected people. Following the injury,
limitations of the ability to move may occur due to the disruption of the motor and sensory functions
of the nervous system depending on the severity of the lesion. An active stance-control
knee-ankle-foot orthosis was developed and tested in earlier works to aid incomplete SCI subjects
by increasing their mobility and independence. This thesis aims at the incorporation of
elastic actuation into the active orthosis to utilise advantages of the compliant system regarding
efficiency and human-robot interaction as well as the reproduction of the phyisological compliance
of the human joints. Therefore, a model-based procedure is adapted to the design of
an elastic actuation system for a gait-assisitve active orthosis. A determination of the optimal
structure and parameters is undertaken via optimisation of models representing compliant actuators
with increasing level of detail. The minimisation of the energy calculated from the positive
amount of power or from the absolute power of the actuator generating one human-like gait cycle
yields an optimal series stiffness, which is similar to the physiological stiffness of the human
knee during the stance phase. Including efficiency factors for components, especially the consideration
of the electric model of an electric motor yields additional information. A human-like
gait cycle contains high torque and low velocities in the stance phase and lower torque combined
with high velocities during the swing. Hence, the efficiency of an electric motor with a gear unit
is only high in one of the phases. This yields a conceptual design of a series elastic actuator with
locking of the actuator position during the stance phase. The locked position combined with the
series compliance allows a reproduction of the characteristics of the human gait cycle during
the stance phase. Unlocking the actuator position for the swing phase enables the selection of
an optimal gear ratio to maximise the recuperable energy. To evaluate the developed concept,
a laboratory specimen based on an electric motor, a harmonic drive gearbox, a torsional series
spring and an electromagnetic brake is designed and appropriate components are selected. A
control strategy, based on impedance control, is investigated and extended with a finite state
machine to activate the locking mechanism. The control scheme and the laboratory specimen
are implemented at a test bench, modelling the foot and shank as a pendulum articulated at the
knee. An identification of parameters yields high and nonlinear friction as a problem of the system,
which reduces the energy efficiency of the system and requires appropriate compensation.
A comparison between direct and elastic actuation shows similar results for both systems at the
test bench, showing that the increased complexity due to the second degree of freedom and
the elastic behaviour of the actuator is treated properly. The final proof of concept requires the
implementation at the active orthosis to emulate uncertainties and variations occurring during
the human gait
Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras
Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.<br/
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