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Sökning: id:"swepub:oai:research.chalmers.se:79b6ec79-8de5-457b-8e99-58e164c12964" > NTU RGB+D 120: A La...

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

Liu, Jun (författare)
Nanyang Technological University
Shahroudy, Amir, 1981 (författare)
Chalmers tekniska högskola,Chalmers University of Technology
Perez, Mauricio (författare)
Nanyang Technological University
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Wang, Gang (författare)
Duan, Ling-Yu (författare)
Peng Cheng Laboratory,Beijing University of Technology
Kot, Alex C. (författare)
Nanyang Technological University
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 (creator_code:org_t)
2020
2020
Engelska.
Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 1939-3539 .- 0162-8828. ; 42:10, s. 2684-2701
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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.

Ämnesord

SAMHÄLLSVETENSKAP  -- Psykologi -- Psykologi (hsv//swe)
SOCIAL SCIENCES  -- Psychology -- Psychology (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Arbetsterapi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Occupational Therapy (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)

Nyckelord

Skeleton
Cameras
3D action recognition
large-scale benchmark
RGB plus D vision
Activity understanding
Benchmark testing
deep learning
Three-dimensional displays
Lighting
Semantics
video analysis
Deep learning

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