In this paper, we focus on heterogeneous feature learning for RGB-D activity recognition.
Considering that features from different channels could share some similar hidden …

The recently developed depth imaging technologies have provided new directions for
human activity recognition (HAR) without attaching optical markers or any other motion …

Human action recognition is an important yet challenging task. The recently developed
commodity depth sensors open up new possibilities of dealing with this problem but also …

Human action recognition is an important yet challenging task. Human actions usually
involve human-object interactions, highly articulated motions, high intra-class variations, and …

Digitization of human motion using skeleton representations offers exciting possibilities for a
large number of applications but, at the same time, requires innovative techniques for their …

Human action recognition under low observational latency is receiving a growing interest in
computer vision due to rapidly develo** technologies in human-robot interaction …

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

We present an unsupervised representation learning approach that compactly encodes the
motion dependencies in videos. Given a pair of images from a video clip, our framework …

Rapid growth and recent developments in education sector and information technologies
have promoted E-learning and collaborative sessions among the learning communities and …

We propose a set of features derived from skeleton tracking of the human body and depth
maps for the purpose of action recognition. The descriptors proposed are easy to implement …