1,122 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
Novel Architecture for Human Re-Identification with a Two-Stream Neural Network and Attention Mechanism
This paper proposes a novel architecture that utilises an attention mechanism in conjunction with multi-stream convolutional neural networks (CNN) to obtain high accuracy in human re-identification (Reid). The proposed architecture consists of four blocks. First, the pre-processing block prepares the input data and feeds it into a spatial-temporal two-stream CNN (STC) with two fusion points that extract the spatial-temporal features. Next, the spatial-temporal attentional LSTM block (STA) automatically fine-tunes the extracted features and assigns weight to the more critical frames in the video sequence by using an attention mechanism. Extensive experiments on four of the most popular datasets support our architecture. Finally, the results are compared with the state of the art, which shows the superiority of this approach
Novel architecture for human re-identification with a two-stream neural network and attention ,echanism
This paper proposes a novel architecture that utilises an attention mechanism in conjunction with multi-stream convolutional neural networks (CNN) to obtain high accuracy in human re-identification (Reid). The proposed architecture consists of four blocks. First, the pre-processing block prepares the input data and feeds it into a spatial-temporal two-stream CNN (STC) with two fusion points that extract the spatial-temporal features. Next, the spatial-temporal attentional LSTM block (STA) automatically fine-tunes the extracted features and assigns weight to the more critical frames in the video sequence by using an attention mechanism. Extensive experiments on four of the most popular datasets support our architecture. Finally, the results are compared with the state of the art, which shows the superiority of this approach
A spatiotemporal deep learning approach for automatic pathological Gait classification
Human motion analysis provides useful information for the diagnosis and recovery assessment of people suffering from pathologies, such as those affecting the way of walking, i.e., gait. With recent developments in deep learning, state-of-the-art performance can now be achieved using a single 2D-RGB-camera-based gait analysis system, offering an objective assessment of gait-related pathologies. Such systems provide a valuable complement/alternative to the current standard practice of subjective assessment. Most 2D-RGB-camera-based gait analysis approaches rely on compact gait representations, such as the gait energy image, which summarize the characteristics of a walking sequence into one single image. However, such compact representations do not
fully capture the temporal information and dependencies between successive gait movements. This limitation is addressed by proposing a spatiotemporal deep learning approach that uses a selection of key frames to represent a gait cycle. Convolutional and recurrent deep neural networks were combined, processing each gait cycle as a collection of silhouette key frames, allowing the system to learn temporal patterns among the spatial features extracted at individual time instants. Trained with gait sequences from the GAIT-IT dataset, the proposed system is able to improve gait pathology classification accuracy, outperforming state-of-the-art solutions and achieving improved generalization on cross-dataset tests.info:eu-repo/semantics/publishedVersio
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
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