5,924 research outputs found
Recurrent Attention Models for Depth-Based Person Identification
We present an attention-based model that reasons on human body shape and
motion dynamics to identify individuals in the absence of RGB information,
hence in the dark. Our approach leverages unique 4D spatio-temporal signatures
to address the identification problem across days. Formulated as a
reinforcement learning task, our model is based on a combination of
convolutional and recurrent neural networks with the goal of identifying small,
discriminative regions indicative of human identity. We demonstrate that our
model produces state-of-the-art results on several published datasets given
only depth images. We further study the robustness of our model towards
viewpoint, appearance, and volumetric changes. Finally, we share insights
gleaned from interpretable 2D, 3D, and 4D visualizations of our model's
spatio-temporal attention.Comment: Computer Vision and Pattern Recognition (CVPR) 201
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
Parsing is All You Need for Accurate Gait Recognition in the Wild
Binary silhouettes and keypoint-based skeletons have dominated human gait
recognition studies for decades since they are easy to extract from video
frames. Despite their success in gait recognition for in-the-lab environments,
they usually fail in real-world scenarios due to their low information entropy
for gait representations. To achieve accurate gait recognition in the wild,
this paper presents a novel gait representation, named Gait Parsing Sequence
(GPS). GPSs are sequences of fine-grained human segmentation, i.e., human
parsing, extracted from video frames, so they have much higher information
entropy to encode the shapes and dynamics of fine-grained human parts during
walking. Moreover, to effectively explore the capability of the GPS
representation, we propose a novel human parsing-based gait recognition
framework, named ParsingGait. ParsingGait contains a Convolutional Neural
Network (CNN)-based backbone and two light-weighted heads. The first head
extracts global semantic features from GPSs, while the other one learns mutual
information of part-level features through Graph Convolutional Networks to
model the detailed dynamics of human walking. Furthermore, due to the lack of
suitable datasets, we build the first parsing-based dataset for gait
recognition in the wild, named Gait3D-Parsing, by extending the large-scale and
challenging Gait3D dataset. Based on Gait3D-Parsing, we comprehensively
evaluate our method and existing gait recognition methods. The experimental
results show a significant improvement in accuracy brought by the GPS
representation and the superiority of ParsingGait. The code and dataset are
available at https://gait3d.github.io/gait3d-parsing-hp .Comment: 16 pages, 14 figures, ACM MM 2023 accepted, project page:
https://gait3d.github.io/gait3d-parsing-h
Multi-level Adversarial Spatio-temporal Learning for Footstep Pressure based FoG Detection
Freezing of gait (FoG) is one of the most common symptoms of Parkinson's
disease, which is a neurodegenerative disorder of the central nervous system
impacting millions of people around the world. To address the pressing need to
improve the quality of treatment for FoG, devising a computer-aided detection
and quantification tool for FoG has been increasingly important. As a
non-invasive technique for collecting motion patterns, the footstep pressure
sequences obtained from pressure sensitive gait mats provide a great
opportunity for evaluating FoG in the clinic and potentially in the home
environment. In this study, FoG detection is formulated as a sequential
modelling task and a novel deep learning architecture, namely Adversarial
Spatio-temporal Network (ASTN), is proposed to learn FoG patterns across
multiple levels. A novel adversarial training scheme is introduced with a
multi-level subject discriminator to obtain subject-independent FoG
representations, which helps to reduce the over-fitting risk due to the high
inter-subject variance. As a result, robust FoG detection can be achieved for
unseen subjects. The proposed scheme also sheds light on improving
subject-level clinical studies from other scenarios as it can be integrated
with many existing deep architectures. To the best of our knowledge, this is
one of the first studies of footstep pressure-based FoG detection and the
approach of utilizing ASTN is the first deep neural network architecture in
pursuit of subject-independent representations. Experimental results on 393
trials collected from 21 subjects demonstrate encouraging performance of the
proposed ASTN for FoG detection with an AUC 0.85
Home detection of freezing of gait using Support Vector Machines through a single waist-worn triaxial accelerometer
Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.Peer ReviewedPostprint (published version
Cyclostationary Processes on Shape Spaces for Gait-Based Recognition
Abstract. We present a geometric and statistical approach to gaitbased human recognition. The novelty here is to consider observations of gait, considered as planar silhouettes, to be cyclostationary processes on a shape space of simple closed curves. Consequently, gait analysis reduces to quantifying differences between underlying stochastic processes using their observations. Individual shapes can be compared using geodesic lengths, but the comparison of gait cycles requires tools for extraction, interpolation, registration, and averaging of individual gait cycles before comparisons. The main steps in our approach are: (i) off-line extraction of human silhouettes from IR video data, (ii) use of piecewise-geodesic paths, connecting the observed shapes, to smoothly interpolate between them, (iii) computation of an average gait cycle within class (i.e. associated with a person) using Karcher means, (iv) registration of average cycles using linear and nonlinear time scaling, (iv) comparisons of average cycles using geodesic lengths between the corresponding shapes. We illustrate this approach on gait sequence obtained from infrared video clips. Experimental results are presented for a data set of 26 subjects.
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