2,822 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
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
In-Place Gestures Classification via Long-term Memory Augmented Network
In-place gesture-based virtual locomotion techniques enable users to control
their viewpoint and intuitively move in the 3D virtual environment. A key
research problem is to accurately and quickly recognize in-place gestures,
since they can trigger specific movements of virtual viewpoints and enhance
user experience. However, to achieve real-time experience, only short-term
sensor sequence data (up to about 300ms, 6 to 10 frames) can be taken as input,
which actually affects the classification performance due to limited
spatio-temporal information. In this paper, we propose a novel long-term memory
augmented network for in-place gestures classification. It takes as input both
short-term gesture sequence samples and their corresponding long-term sequence
samples that provide extra relevant spatio-temporal information in the training
phase. We store long-term sequence features with an external memory queue. In
addition, we design a memory augmented loss to help cluster features of the
same class and push apart features from different classes, thus enabling our
memory queue to memorize more relevant long-term sequence features. In the
inference phase, we input only short-term sequence samples to recall the stored
features accordingly, and fuse them together to predict the gesture class. We
create a large-scale in-place gestures dataset from 25 participants with 11
gestures. Our method achieves a promising accuracy of 95.1% with a latency of
192ms, and an accuracy of 97.3% with a latency of 312ms, and is demonstrated to
be superior to recent in-place gesture classification techniques. User study
also validates our approach. Our source code and dataset will be made available
to the community.Comment: This paper is accepted to IEEE ISMAR202
Gender Perception From Gait: A Comparison Between Biological, Biomimetic and Non-biomimetic Learning Paradigms
This paper explores in parallel the underlying mechanisms in human perception of biological motion and the best approaches for automatic classification of gait. The experiments tested three different learning paradigms, namely, biological, biomimetic, and non-biomimetic models for gender identification from human gait. Psychophysical experiments with twenty-one observers were conducted along with computational experiments without applying any gender specific modifications to the models or the stimuli. Results demonstrate the utilization of a generic memory based learning system in humans for gait perception, thus reducing ambiguity between two opposing learning systems proposed for biological motion perception. Results also support the biomimetic nature of memory based artificial neural networks (ANN) in their ability to emulate biological neural networks, as opposed to non-biomimetic models. In addition, the comparison between biological and computational learning approaches establishes a memory based biomimetic model as the best candidate for a generic artificial gait classifier (83% accuracy, p < 0.001), compared to human observers (66%, p < 0.005) or non-biomimetic models (83%, p < 0.001) while adhering to human-like sensitivity to gender identification, promising potential for application of the model in any given non-gender based gait perception objective with superhuman performance
Remote Gait type classification system using markerless 2D video
Several pathologies can alter the way people walk, i.e., their gait. Gait analysis can be used to detect such alterations and, therefore, help diagnose certain pathologies or assess people’s health and recovery. Simple vision-based systems have a considerable potential in this area, as they allow the capture of gait in unconstrained environments, such as at home or in a clinic, while the required computations can be done remotely. State-of-the-art vision-based systems for gait analysis use deep learning strategies, thus requiring a large amount of data for training. However, to the best of our knowledge, the largest publicly available pathological gait dataset contains only 10 subjects, simulating 5 types of gait.
This paper presents a new dataset, GAIT-IT, captured from 21 subjects simulating 5 types of gait, at 2 severity levels. The dataset is recorded in a professional studio, making the sequences free of background camouflage, variations in illumination and other visual artifacts. The dataset is used to train a novel automatic gait analysis system. Compared to the state-of-the-art, the proposed system achieves a drastic reduction in the number of trainable parameters, memory requirements and execution times, while the classification accuracy is on par with the state-of-the-art.
Recognizing the importance of remote healthcare, the proposed automatic gait analysis system is integrated with a prototype web application. This prototype is presently hosted in a private network, and after further tests and development it will allow people to upload a video of them walking and execute a web service that classifies their gait. The web application has a user-friendly interface usable by healthcare professionals or by laypersons. The application also makes an association between the identified type of gait and potential gait pathologies that exhibit the identified characteristics.info:eu-repo/semantics/publishedVersio
Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification
Gait-based person re-identification (Re-ID) is valuable for safety-critical
applications, and using only 3D skeleton data to extract discriminative gait
features for person Re-ID is an emerging open topic. Existing methods either
adopt hand-crafted features or learn gait features by traditional supervised
learning paradigms. Unlike previous methods, we for the first time propose a
generic gait encoding approach that can utilize unlabeled skeleton data to
learn gait representations in a self-supervised manner. Specifically, we first
propose to introduce self-supervision by learning to reconstruct input skeleton
sequences in reverse order, which facilitates learning richer high-level
semantics and better gait representations. Second, inspired by the fact that
motion's continuity endows temporally adjacent skeletons with higher
correlations ("locality"), we propose a locality-aware attention mechanism that
encourages learning larger attention weights for temporally adjacent skeletons
when reconstructing current skeleton, so as to learn locality when encoding
gait. Finally, we propose Attention-based Gait Encodings (AGEs), which are
built using context vectors learned by locality-aware attention, as final gait
representations. AGEs are directly utilized to realize effective person Re-ID.
Our approach typically improves existing skeleton-based methods by 10-20%
Rank-1 accuracy, and it achieves comparable or even superior performance to
multi-modal methods with extra RGB or depth information. Our codes are
available at https://github.com/Kali-Hac/SGE-LA.Comment: Accepted at IJCAI 2020 Main Track. Sole copyright holder is IJCAI.
Codes are available at https://github.com/Kali-Hac/SGE-L
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