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.

本文介绍了一个包含超过 56,000 个视频样本和 4 百万帧的大规模 RGB + D 人体动作识别数据集，其中包括 60 种不同的行为类别，并提出了一种新的循环神经网络结构来更好地对每个身体部位的特征进行长期时间相关性建模。实验结果表明，在跨主体和跨视图评估标准上，应用深度学习方法比最先进的手工制作特征具有优势。