Human activity recognition (HAR) is a very active yet challenging and demanding area of
computer science. Due to the articulated nature of human motion, it is not trivial to detect …

This work develops a continuous sign language (SL) recognition framework with deep
neural networks, which directly transcribes videos of SL sentences to sequences of ordered …

In this paper, a comparative experimental assessment of computer vision-based methods for
sign language recognition is conducted. By implementing the most recent deep neural …

This work presents a weakly supervised framework with deep neural networks for vision-
based continuous sign language recognition, where the ordered gloss labels but no exact …

This work presents a statistical recognition approach performing large vocabulary
continuous sign language recognition across different signers. Automatic sign language …

Continuous sign language recognition (SLR) is a challenging task that requires learning on
both spatial and temporal dimensions of signing frame sequences. Most recent work …

We present a deep-learning framework for real-time multiple spatio-temporal (S/T) action
localisation and classification. Current state-of-the-art approaches work offline, and are too …

We present a method for gesture detection and localisation based on multi-scale and multi-
modal deep learning. Each visual modality captures spatial information at a particular spatial …

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

In this work, we propose an approach to the spatiotemporal localisation (detection) and
classification of multiple concurrent actions within temporally untrimmed videos. Our …