NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
Artikel i vetenskaplig tidskrift, 2020

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.




3D action recognition

video analysis

Deep learning

large-scale benchmark

Benchmark testing

Activity understanding

deep learning


Three-dimensional displays

RGB plus D vision


Jun Liu

Nanyang Technological University

Amir Shahroudy

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Mauricio Perez

Nanyang Technological University

Gang Wang

Alibaba Group Holding Limited

Ling-Yu Duan

Peng Cheng Laboratory

Beijing University of Technology

Alex C. Kot

Nanyang Technological University

IEEE Transactions on Pattern Analysis and Machine Intelligence

0162-8828 (ISSN)

Vol. 42 10 2684-2701


Psykologi (exklusive tillämpad psykologi)


Datorseende och robotik (autonoma system)





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