Most of the existing deep learning algorithms are supervised and rely on a tremendous number manually labeled samples. However, in most domains, due to scarcity samples or excessive cost labeling, it would be impracticable provide numerous training network. In this paper, few-shot video classification network termed Hierarchical Motion Excitation Network (HME-Net) is proposed from perspective a...

The performance of deep learning (DL) algorithms for radar-based human motion recognition (HMR) is hindered by the diversity and volume available training data. In this article, to tackle issue insufficient data HMR, we propose an instance-based transfer (ITL) method with limited radar micro-Doppler (MD) signatures, alleviating burden collecting annotating a large number samples. ITL unique alg...

Motivation, Problem Statement, Related Works This paper proposes a discovery algorithm of knowledge of remarkable motion features in daily life action recognition based on Support Vector Machine. The main characteristics of the proposed method are 1)basic scheme of the algorithm is based on Support Vector Learning and its generalization error, 2)remarkable motion features are discovered in resp...

In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for human motion recognition implemented on a ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human motion recognition system with simple motion feat...

Correspondence Sahil Narang, University of North Carolina, Chapel Hill, NC, USA. Email: [email protected] Abstract Current 3D capture and modeling technology can rapidly generate highly photorealistic 3D avatars of human subjects. However, while the avatars look like their human counterparts, their movements often do not mimic their own due to existing challenges in accurate motion capture and r...