  Recent approaches in depth-based human activity analysis achieved outstanding
performance and proved the effectiveness of 3D representation for
classification of action classes. Currently available depth-based and
RGB+D-based action recognition benchmarks have a number of limitations,
including the lack of training samples, distinct class labels, camera views and
variety of subjects. In this paper we introduce a large-scale dataset for RGB+D
human action recognition with more than 56 thousand video samples and 4 million
frames, collected from 40 distinct subjects. Our dataset contains 60 different
action classes including daily, mutual, and health-related actions. In
addition, we propose a new recurrent neural network structure to model the
long-term temporal correlation of the features for each body part, and utilize
them for better action classification. Experimental results show the advantages
of applying deep learning methods over state-of-the-art hand-crafted features
on the suggested cross-subject and cross-view evaluation criteria for our
dataset. The introduction of this large scale dataset will enable the community
to apply, develop and adapt various data-hungry learning techniques for the
task of depth-based and RGB+D-based human activity analysis.
