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231224s2017 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2016.2565479
|2 doi
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|a eng
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| 100 |
1 |
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|a Yang, Xiaodong
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Super Normal Vector for Human Activity Recognition with Depth Cameras
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| 264 |
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|c 2017
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 18.10.2018
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|a Date Revised 18.10.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a The advent of cost-effectiveness and easy-operation depth cameras has facilitated a variety of visual recognition tasks including human activity recognition. This paper presents a novel framework for recognizing human activities from video sequences captured by depth cameras. We extend the surface normal to polynormal by assembling local neighboring hypersurface normals from a depth sequence to jointly characterize local motion and shape information. We then propose a general scheme of super normal vector (SNV) to aggregate the low-level polynormals into a discriminative representation, which can be viewed as a simplified version of the Fisher kernel representation. In order to globally capture the spatial layout and temporal order, an adaptive spatio-temporal pyramid is introduced to subdivide a depth video into a set of space-time cells. In the extensive experiments, the proposed approach achieves superior performance to the state-of-the-art methods on the four public benchmark datasets, i.e., MSRAction3D, MSRDailyActivity3D, MSRGesture3D, and MSRActionPairs3D
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| 650 |
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|a Journal Article
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| 650 |
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|a Research Support, U.S. Gov't, Non-P.H.S.
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| 700 |
1 |
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|a Tian, YingLi
|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 39(2017), 5 vom: 15. Mai, Seite 1028-1039
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|x 1939-3539
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