287 research outputs found
Bradford Multi-Modal Gait Database: Gateway to Using Static Measurements to Create a Dynamic Gait Signature
YesAims: To create a gait database with optimum accuracy of joint rotational data and an accu-rate
representation of 3D volume, and explore the potential of using the database in studying the
relationship between static and dynamic features of a human’s gait.
Study Design: The study collected gait samples from 38 subjects, in which they were asked to
walk, run, walk to run transition, and walk with a bag. The motion capture, video, and 3d
measurement data extracted was used to analyse and build a correlation between features.
Place and Duration of Study: The study was conducted in the University of Bradford. With the
ethical approval from the University, 38 subjects’ motion and body volumes were recorded at the
motion capture studio from May 2011- February 2013.
Methodology: To date, the database includes 38 subjects (5 females, 33 males) conducting walk
cycles with speed and load as covariants. A correlation analysis was conducted to ex-plore the
potential of using the database to study the relationship between static and dynamic features. The
volumes and surface area of body segments were used as static features. Phased-weighted
magnitudes extracted through a Fourier transform of the rotation temporal data of the joints from the motion capture were used as dynamic features. The Pearson correlation coefficient is used to
evaluate the relationship between the two sets of data.
Results: A new database was created with 38 subjects conducting four forms of gait (walk, run,
walk to run, and walking with a hand bag). Each subject recording included a total of 8 samples of
each form of gait, and a 3D point cloud (representing the 3D volume of the subject). Using a Pvalue
(P<.05) as a criterion for statistical significance, 386 pairs of features displayed a strong
relationship.
Conclusion: A novel database available to the scientific community has been created. The
database can be used as an ideal benchmark to apply gait recognition techniques, and based on
the correlation analysis, can offer a detailed perspective of the dynamics of gait and its relationship
to volume. Further research in the relationship between static and dynamic features can contribute
to the field of biomechanical analysis, use of biometrics in forensic applications, and 3D virtual walk
simulation
Topological signature for periodic motion recognition
In this paper, we present an algorithm that computes the topological
signature for a given periodic motion sequence. Such signature consists of a
vector obtained by persistent homology which captures the topological and
geometric changes of the object that models the motion. Two topological
signatures are compared simply by the angle between the corresponding vectors.
With respect to gait recognition, we have tested our method using only the
lowest fourth part of the body's silhouette. In this way, the impact of
variations in the upper part of the body, which are very frequent in real
scenarios, decreases considerably. We have also tested our method using other
periodic motions such as running or jumping. Finally, we formally prove that
our method is robust to small perturbations in the input data and does not
depend on the number of periods contained in the periodic motion sequence.Comment: arXiv admin note: substantial text overlap with arXiv:1707.0698
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
Vision-based techniques for gait recognition
Global security concerns have raised a proliferation of video surveillance
devices. Intelligent surveillance systems seek to discover possible threats
automatically and raise alerts. Being able to identify the surveyed object can
help determine its threat level. The current generation of devices provide
digital video data to be analysed for time varying features to assist in the
identification process. Commonly, people queue up to access a facility and
approach a video camera in full frontal view. In this environment, a variety of
biometrics are available - for example, gait which includes temporal features
like stride period. Gait can be measured unobtrusively at a distance. The video
data will also include face features, which are short-range biometrics. In this
way, one can combine biometrics naturally using one set of data. In this paper
we survey current techniques of gait recognition and modelling with the
environment in which the research was conducted. We also discuss in detail the
issues arising from deriving gait data, such as perspective and occlusion
effects, together with the associated computer vision challenges of reliable
tracking of human movement. Then, after highlighting these issues and
challenges related to gait processing, we proceed to discuss the frameworks
combining gait with other biometrics. We then provide motivations for a novel
paradigm in biometrics-based human recognition, i.e. the use of the
fronto-normal view of gait as a far-range biometrics combined with biometrics
operating at a near distance
Gait Recognition: Databases, Representations, and Applications
There has been considerable progress in automatic recognition of people by the way they walk since its inception almost 20 years ago: there is now a plethora of technique and data which continue to show that a person’s walking is indeed unique. Gait recognition is a behavioural biometric which is available even at a distance from a camera when other biometrics may be occluded, obscured or suffering from insufficient image resolution (e.g. a blurred face image or a face image occluded by mask). Since gait recognition does not require subject cooperation due to its non-invasive capturing process, it is expected to be applied for criminal investigation from CCTV footages in public and private spaces. This article introduces current progress, a research background, and basic approaches for gait recognition in the first three sections, and two important aspects of gait recognition, the gait databases and gait feature representations are described in the following sections.Publicly available gait databases are essential for benchmarking individual approaches, and such databases should contain a sufficient number of subjects as well as covariate factors to realize statistically reliable performance evaluation and also robust gait recognition. Gait recognition researchers have therefore built such useful gait databases which incorporate subject diversities and/or rich covariate factors.Gait feature representation is also an important aspect for effective and efficient gait recognition. We describe the two main approaches to representation: model-free (appearance-based) approaches and model-based approaches. In particular, silhouette-based model-free approaches predominate in recent studies and many have been proposed and are described in detail.Performance evaluation results of such recent gait feature representations on two of the publicly available gait databases are reported: USF Human ID with rich covariate factors such as views, surface, bag, shoes, time elapse; and OU-ISIR LP with more than 4,000 subjects. Since gait recognition is suitable for criminal investigation applications of the gait recognition to forensics are addressed with real criminal cases in the application section. Finally, several open problems of the gait recognition are discussed to show future research avenues of the gait recognition
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
GaitFormer: Revisiting Intrinsic Periodicity for Gait Recognition
Gait recognition aims to distinguish different walking patterns by analyzing
video-level human silhouettes, rather than relying on appearance information.
Previous research on gait recognition has primarily focused on extracting local
or global spatial-temporal representations, while overlooking the intrinsic
periodic features of gait sequences, which, when fully utilized, can
significantly enhance performance. In this work, we propose a plug-and-play
strategy, called Temporal Periodic Alignment (TPA), which leverages the
periodic nature and fine-grained temporal dependencies of gait patterns. The
TPA strategy comprises two key components. The first component is Adaptive
Fourier-transform Position Encoding (AFPE), which adaptively converts features
and discrete-time signals into embeddings that are sensitive to periodic
walking patterns. The second component is the Temporal Aggregation Module
(TAM), which separates embeddings into trend and seasonal components, and
extracts meaningful temporal correlations to identify primary components, while
filtering out random noise. We present a simple and effective baseline method
for gait recognition, based on the TPA strategy. Extensive experiments
conducted on three popular public datasets (CASIA-B, OU-MVLP, and GREW)
demonstrate that our proposed method achieves state-of-the-art performance on
multiple benchmark tests
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