NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis

• Published in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 1010 - 1019
• Main Authors: Shahroudy, Amir, Jun Liu, Tian-Tsong Ng, Gang Wang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2016
• Subjects: Benchmark testing; Cameras; Feature extraction; Learning systems; Recurrent neural networks; Skeleton; Three-dimensional displays
• ISSN: 1063-6919
• DOI: 10.1109/CVPR.2016.115

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+Dbased 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 handcrafted 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.