554 research outputs found
VN-GAN: Identity-preserved Variation Normalizing GAN for Gait Recognition
© 2019 IEEE. Gait is recognized as a unique biometric characteristic to identify a walking person remotely across surveillance networks. However, the performance of gait recognition severely suffers challenges from view angle diversity. To address the problem, an identity-preserved Variation Normalizing Generative Adversarial Network (VN-GAN) is proposed for learning purely identity-related representations. It adopts a coarse-to-fine manner which firstly generates initial coarse images by normalizing view to an identical one and then refines the coarse images by injecting identity-related information. In specific, Siamese structure with discriminators for both camera view angles and human identities is utilized to achieve variation normalization and identity preservation of two stages, respectively. In addition to discriminators, reconstruction loss and identity-preserving loss are integrated, which forces the generated images to be the same in view and to be discriminative in identity. This ensures to generate identity-related images in an identical view of good visual effect for gait recognition. Extensive experiments on benchmark datasets demonstrate that the proposed VN-GAN can generate visually interpretable results and achieve promising performance for gait recognition
Person recognition based on deep gait: a survey.
Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future
Human Gait Recognition Subject to Different Covariate Factors in a Multi-View Environment
Human gait recognition system identifies individuals based on their biometric traits. A human’s biometric features can be grouped into physiologic or behavioral traits. Biometric traits, such as the face [1], ears [2], iris [3], finger prints, passwords, and tokens, require highly accurate recognition and a well-controlled human interaction to be effective. In contrast, behavioral traits such as voice, signature, and gait do not require any human interaction and can be collected in a hidden and non-invasive mode with a camera system at a low resolution. In comparison with other physiological traits, one of the main advantages of gait analysis is the collection of data from a certain distance. However, gait is less powerful than physiological traits, yet it still has widespread application in surveillance for unfavorable situations. From traditional algorithms to deep learning models, a gait survey provides a detailed history of gait recognition
Gait Data Augmentation using Physics-Based Biomechanical Simulation
This paper focuses on addressing the problem of data scarcity for gait
analysis. Standard augmentation methods may produce gait sequences that are not
consistent with the biomechanical constraints of human walking. To address this
issue, we propose a novel framework for gait data augmentation by using
OpenSIM, a physics-based simulator, to synthesize biomechanically plausible
walking sequences. The proposed approach is validated by augmenting the WBDS
and CASIA-B datasets and then training gait-based classifiers for 3D gender
gait classification and 2D gait person identification respectively.
Experimental results indicate that our augmentation approach can improve the
performance of model-based gait classifiers and deliver state-of-the-art
results for gait-based person identification with an accuracy of up to 96.11%
on the CASIA-B dataset.Comment: 30 pages including references, 5 Figures submitted to ESW
Extraction of biomedical indicators from gait videos
Gait has been an extensively investigated topic in recent years. Through the
analysis of gait it is possible to detect pathologies, which makes this analysis very
important to assess anomalies and, consequently, help in the diagnosis and rehabilitation of patients. There are some systems for analyzing gait, but they are
usually either systems with subjective evaluations or systems used in specialized
laboratories with complex equipment, which makes them very expensive and inaccessible. However, there has been a significant effort of making available simpler
and more accurate systems for gait analysis and classification. This dissertation
reviews recent gait analysis and classification systems, presents a new database
with videos of 21 subjects, simulating 4 different pathologies as well as normal
gait, and also presents a web application that allows the user to remotely access
an automatic classification system and thus obtain the expected classification and
heatmaps for the given input. The classification system is based on the use of gait
representation images such as the Gait Energy Image (GEI) and the Skeleton Gait
Energy Image (SEI), which are used as input to a VGG-19 Convolutional Neural
Network (CNN) that is used to perform classification. This classification system
is a vision-based system. To sum up, the developed web application aims to show
the usefulness of the classification system, making it possible for anyone to access
it.A marcha tem sido um tema muito investigado nos últimos anos. Através
da análise da marcha é possível detetar patologias, o que torna esta análise muito
importante para avaliar anómalias e consequentemente, ajudar no diagnóstico e na
reabilitação dos pacientes. Existem alguns sistemas para analisar a marcha, mas
habitualmente, ou estão sujeitos a uma interpretação subjetiva, ou são sistemas
usados em laboratórios especializados com equipamento complexo, o que os torna
muito dispendiosos e inacessíveis. No entanto, tem havido um esforço significativo com o objectivo de disponibilizar sistemas mais simples e mais precisos para
análise e classificação da marcha. Esta dissertação revê os sistemas de análise
e classificação da marcha desenvolvidos recentemente, apresenta uma nova base
de dados com vídeos de 21 sujeitos, a simular 4 patologias diferentes bem como
marcha normal, e apresenta também uma aplicação web que permite ao utilizador
aceder remotamente a um sistema automático de classificação e assim, obter a classificação prevista e mapas de características respectivos de acordo com a entrada
dada. O sistema de classificação baseia-se no uso de imagens de representação da
marcha como a "Gait Energy Image" (GEI) e "Skeleton Gait Energy Image" (SEI),
que são usadas como entrada numa rede neuronal convolucional VGG-19 que é
usada para realizar a classificação. Este sistema de classificação corresponde a um
sistema baseado na visão. Em suma, a aplicação web desenvolvida tem como finalidade mostrar a utilidade do sistema de classificação, tornando possível o acesso a
qualquer pessoa
Unsupervised Video Understanding by Reconciliation of Posture Similarities
Understanding human activity and being able to explain it in detail surpasses
mere action classification by far in both complexity and value. The challenge
is thus to describe an activity on the basis of its most fundamental
constituents, the individual postures and their distinctive transitions.
Supervised learning of such a fine-grained representation based on elementary
poses is very tedious and does not scale. Therefore, we propose a completely
unsupervised deep learning procedure based solely on video sequences, which
starts from scratch without requiring pre-trained networks, predefined body
models, or keypoints. A combinatorial sequence matching algorithm proposes
relations between frames from subsets of the training data, while a CNN is
reconciling the transitivity conflicts of the different subsets to learn a
single concerted pose embedding despite changes in appearance across sequences.
Without any manual annotation, the model learns a structured representation of
postures and their temporal development. The model not only enables retrieval
of similar postures but also temporal super-resolution. Additionally, based on
a recurrent formulation, next frames can be synthesized.Comment: Accepted by ICCV 201
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