Within a large range of applications in computer vision, Human Action Recognition has
become one of the most attractive research fields. Ambiguities in recognizing actions does …

In this paper, we present a method (Action-Fusion) for human action recognition from depth
maps and posture data using convolutional neural networks (CNNs). Two input descriptors …

Upsurging abnormal activities in crowded locations such as airports, train stations, bus
stops, shopping malls, etc., urges the necessity for an intelligent surveillance system. An …

We present a cognitively plausible novel framework capable of learning the grounding in
visual semantics and the grammar of natural language commands given to a robot in a table …

Human action recognition is one of the most important and challenging topic in the fields of
image processing. Unlike object recognition, action recognition requires motion feature …

There is increasing interest in using Qualitative Spatial Relations as a formalism to abstract
from noisy and large amounts of video data in order to form high level conceptualisations, eg …

The success of intelligent mobile robots operating and collaborating with humans in daily
living environments depends on their ability to generalise and learn human movements, and …

The long-term vision for robotics is to have fully autonomous mobile robots that perceive the
environment as humans do or even better. This article transfers the core ideas from human …

To study complex human activity and how it is perceived and remembered, it is valuable to
have large-scale, well-characterized stimuli that are representative of such activity. We …

For autonomous robots to collaborate on joint tasks with humans they require a shared
understanding of an observed scene. We present a method for unsupervised learning of …