NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 42; no. 10; pp. 2684 - 2701
• Main Authors: Liu, Jun, Shahroudy, Amir, Perez, Mauricio, Wang, Gang, Duan, Ling-Yu, Kot, Alex C.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3D action recognition; Activity understanding; Benchmark testing; Benchmarks; Cameras; Datasets; Deep learning; Human activity recognition; Human motion; large-scale benchmark; Lighting; Machine learning; Performance evaluation; RGB plus D vision; RGB+D vision; Semantics; Skeleton; Three-dimensional displays; video analysis
• ISSN: 0162-8828; 1939-3539; 2160-9292; 1939-3539
• DOI: 10.1109/TPAMI.2019.2916873

Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realistic number of distinct class categories, diversity in camera views, varied environmental conditions, and variety of human subjects. In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. Furthermore, we investigate a novel one-shot 3D activity recognition problem on our dataset, and a simple yet effective Action-Part Semantic Relevance-aware (APSR) framework is proposed for this task, which yields promising results for recognition of the novel action classes. We believe the introduction of this large-scale dataset will enable the community to apply, adapt, and develop various data-hungry learning techniques for depth-based and RGB+D-based human activity understanding.