相关论文: Interpretable Gait Recognition by Granger Causalit…
Human actions comprise of joint motion of articulated body parts or `gestures'. Human skeleton is intuitively represented as a sparse graph with joints as nodes and natural connections between them as edges. Graph convolutional networks…
Biometric identification systems have become immensely popular and important because of their high reliability and efficiency. However person identification at a distance, still remains a challenging problem. Gait can be seen as an…
The use of gait for person identification has important advantages such as being non-invasive, unobtrusive, not requiring cooperation and being less likely to be obscured compared to other biometrics. Existing methods for gait recognition…
Hand Gesture Recognition (HGR) is of major importance for Human-Computer Interaction (HCI) applications. In this paper, we present a new hand gesture recognition approach called GNG-IEMD. In this approach, first, we use a Growing Neural Gas…
We introduce a new dynamic model with the capability of recognizing both activities that an individual is performing as well as where that ndividual is located. Our model is novel in that it utilizes a dynamic graphical model to jointly…
The goal of gait recognition is to extract identity-invariant features of an individual under various gait conditions, e.g., cross-view and cross-clothing. Most gait models strive to implicitly learn the common traits across different gait…
This paper describes a method to convert Microsoft Kinect coordinates into gait parameters in order to detect a person's gait change. The proposed method can help quantify the progress of physical therapy. Microsoft Kinect, a popular…
Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Two prominent examples are molecule property prediction and brain connectome analysis. Importantly, recent works…
Biometric authentication using gait has become a promising field due to its unobtrusive nature. Recent approaches in model-based gait recognition techniques utilize spatio-temporal graphs for the elegant extraction of gait features.…
Gait recognition is the characterization of unique biometric patterns associated with each individual which can be utilized to identify a person without direct contact. A public gait database with a relatively large number of subjects can…
Gait recognition is a remote biometric technology that utilizes the dynamic characteristics of human movement to identify individuals even under various extreme lighting conditions. Due to the limitation in spatial perception capability…
Gait analysis, an expanding research area, employs non invasive sensors and machine learning techniques for a range of applicatio ns. In this study, we concentrate on gait analysis for detecting cognitive decline in Parkinson's disease (PD)…
Gait recognition is a crucial biometric identification technique. Camera-based gait recognition has been widely applied in both research and industrial fields. LiDAR-based gait recognition has also begun to evolve most recently, due to the…
Gait recognition using noninvasively acquired data has been attracting an increasing interest in the last decade. Among various modalities of data sources, it is experimentally found that the data involving skeletal representation are…
Gait recognition stands as one of the most pivotal remote identification technologies and progressively expands across research and industry communities. However, existing gait recognition methods heavily rely on task-specific upstream…
The Gaussian graphical model (GGM) incorporates an undirected graph to represent the conditional dependence between variables, with the precision matrix encoding partial correlation between pair of variables given the others. To achieve…
Human pose estimation from monocular video is a rapidly advancing field that offers great promise to human movement science and rehabilitation. This potential is tempered by the smaller body of work ensuring the outputs are clinically…
Detecting walking pattern abnormalities in dairy cows early on holds the potential to reduce the occurrence of clinical lameness. This study aimed to predict gait scores in non-clinically lame dairy cows by using gait attributes based on…
Gait recognition is a valuable biometric task that enables the identification of individuals from a distance based on their walking patterns. However, it remains limited by the lack of large-scale labeled datasets and the difficulty of…
Gait recognition, a rapidly advancing vision technology for person identification from a distance, has made significant strides in indoor settings. However, evidence suggests that existing methods often yield unsatisfactory results when…