24,189 research outputs found
Gait Dynamics for Recognition and Classification
This paper describes a representation of the dynamics of human walking action for the purpose of person identification and classification by gait appearance. Our gait representation is based on simple features such as moments extracted from video silhouettes of human walking motion. We claim that our gait dynamics representation is rich enough for the task of recognition and classification. The use of our feature representation is demonstrated in the task of person recognition from video sequences of orthogonal views of people walking. We demonstrate the accuracy of recognition on gait video sequences collected over different days and times, and under varying lighting environments. In addition, preliminary results are shown on gender classification using our gait dynamics features
Covariate factor mitigation techniques for robust gait recognition
The human gait is a discriminative feature capable of recognising a person by their unique
walking manner. Currently gait recognition is based on videos captured in a controlled
environment. These videos contain challenges, termed covariate factors, which affect the
natural appearance and motion of gait, e.g. carrying a bag, clothing, shoe type and time.
However gait recognition has yet to achieve robustness to these covariate factors.
To achieve enhanced robustness capabilities, it is essential to address the existing gait
recognition limitations. Specifically, this thesis develops an understanding of how covariate
factors behave while a person is in motion and the impact covariate factors have on
the natural appearance and motion of gait. Enhanced robustness is achieved by producing
a combination of novel gait representations and novel covariate factor detection and
removal procedures.
Having addressed the limitations regarding covariate factors, this thesis achieves the goal
of robust gait recognition. Using a skeleton representation of the human figure, the Skeleton
Variance Image condenses a skeleton sequence into a single compact 2D gait representation
to express the natural gait motion. In addition, a covariate factor detection
and removal module is used to maximise the mitigation of covariate factor effects. By
establishing the average pixel distribution within training (covariate factor free) representations,
a comparison against test (covariate factor) representations achieves effective
covariate factor detection. The corresponding difference can effectively remove covariate
factors which occur at the boundary of, and hidden within, the human figure.The Engineering and Physical Sciences Research Council (EPSRC
GaitPT: Skeletons Are All You Need For Gait Recognition
The analysis of patterns of walking is an important area of research that has
numerous applications in security, healthcare, sports and human-computer
interaction. Lately, walking patterns have been regarded as a unique
fingerprinting method for automatic person identification at a distance. In
this work, we propose a novel gait recognition architecture called Gait Pyramid
Transformer (GaitPT) that leverages pose estimation skeletons to capture unique
walking patterns, without relying on appearance information. GaitPT adopts a
hierarchical transformer architecture that effectively extracts both spatial
and temporal features of movement in an anatomically consistent manner, guided
by the structure of the human skeleton. Our results show that GaitPT achieves
state-of-the-art performance compared to other skeleton-based gait recognition
works, in both controlled and in-the-wild scenarios. GaitPT obtains 82.6%
average accuracy on CASIA-B, surpassing other works by a margin of 6%.
Moreover, it obtains 52.16% Rank-1 accuracy on GREW, outperforming both
skeleton-based and appearance-based approaches
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
Entropy Volumes for Viewpoint Independent Gait Recognition
Gait as biometrics has been widely used for
human identi cation. However, direction changes cause
di culties for most of the gait recognition systems, due
to appearance changes. This study presents an e cient
multi-view gait recognition method that allows curved
trajectories on completely unconstrained paths for in-
door environments. Our method is based on volumet-
ric reconstructions of humans, aligned along their way.
A new gait descriptor, termed as Gait Entropy Vol-
ume (GEnV), is also proposed. GEnV focuses on cap-
turing 3D dynamical information of walking humans
through the concept of entropy. Our approach does
not require the sequence to be split into gait cycles.
A GEnV based signature is computed on the basis of
the previous 3D gait volumes. Each signature is clas-
si ed by a Support Vector Machine, and a majority
voting policy is used to smooth and reinforce the clas-
si cations results. The proposed approach is experimen-
tally validated on the \AVA Multi-View Gait Dataset
(AVAMVG)" and on the \Kyushu University 4D Gait
Database (KY4D)". The results show that this new ap-
proach achieves promising results in the problem of gait
recognition on unconstrained paths
Combining the Silhouette and Skeleton Data for Gait Recognition
Gait recognition, a promising long-distance biometric technology, has aroused
intense interest in computer vision. Existing works on gait recognition can be
divided into appearance-based methods and model-based methods, which extract
features from silhouettes and skeleton data, respectively. However, since
appearance-based methods are greatly affected by clothing changing and carrying
condition, and model-based methods are limited by the accuracy of pose
estimation approaches, gait recognition remains challenging in practical
applications. In order to integrate the advantages of such two approaches, a
two-branch neural network (NN) is proposed in this paper. Our method contains
two branches, namely a CNN-based branch taking silhouettes as input and a
GCN-based branch taking skeletons as input. In addition, two new modules are
proposed in the GCN-based branch for better gait representation. First, we
present a simple yet effective fully connected graph convolution operator to
integrate the multi-scale graph convolutions and alleviate the dependence on
natural human joint connections. Second, we deploy a multi-dimension attention
module named STC-Att to learn spatial, temporal and channel-wise attention
simultaneously. We evaluated the proposed two-branch neural network on the
CASIA-B dataset. The experimental results show that our method achieves
state-of-the-art performance in various conditions.Comment: The paper is under consideration at Computer Vision and Image
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