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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2019.2916873
|2 doi
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Liu, Jun
|e verfasserin
|4 aut
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| 245 |
1 |
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|a NTU RGB+D 120
|b A Large-Scale Benchmark for 3D Human Activity Understanding
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| 264 |
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|c 2020
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|a Text
|b txt
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|a Date Completed 25.06.2021
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|a Date Revised 25.06.2021
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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| 520 |
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|a 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
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Shahroudy, Amir
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Perez, Mauricio
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Gang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Duan, Ling-Yu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kot, Alex C
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 42(2020), 10 vom: 01. Okt., Seite 2684-2701
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|x 1939-3539
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|g volume:42
|g year:2020
|g number:10
|g day:01
|g month:10
|g pages:2684-2701
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2019.2916873
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