In recent years, gesture recognition has been widely used in the fields of intelligent driving,
virtual reality, and human-computer interaction. With the development of artificial …

Skeleton-based human action recognition has attracted a lot of research attention during the
past few years. Recent works attempted to utilize recurrent neural networks to model the …

Recently, skeleton based action recognition gains more popularity due to cost-effective
depth sensors coupled with real-time skeleton estimation algorithms. Traditional approaches …

Recent advances in physical Human-Robot Interaction (pHRI) have shown the potential and
feasibility of robot systems for active and safe collaboration with humans. This induces high …

In this paper we present a method to capture video-wide temporal information for action
recognition. We postulate that a function capable of ordering the frames of a video …

Temporal dynamics of postures over time is crucial for sequence-based action recognition.
Human actions can be represented by the corresponding motions of articulated skeleton …

We propose a function-based temporal pooling method that captures the latent structure of
the video sequence data-eg, how frame-level features evolve over time in a video. We show …

This paper describes a novel method called Deep Dynamic Neural Networks (DDNN) for
multimodal gesture recognition. A semi-supervised hierarchical dynamic framework based …

The interest in action and gesture recognition has grown considerably in the last years. In
this paper, we present a survey on current deep learning methodologies for action and …

Over the last few years, with the immense popularity of the Kinect, there has been renewed
interest in developing methods for human gesture and action recognition from 3D skeletal …