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Cited by 6,235

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

3D Convolutional Neural Networks for Human Action Recognition

Ji, Shuiwang, Xu, Wei, Yang, Ming, Yu, Kai·2013·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/tpami.2012.59
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Abstract

We consider the automated recognition of human actions in surveillance videos. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. Convolutional neural networks (CNNs) are a type of deep model that can act directly on the raw inputs. However, such models are currently limited to handling 2D inputs. In this paper, we develop a novel 3D CNN model for action recognition. This model extracts features from both the spatial and the temporal dimensions by performing 3D convolutions, thereby capturing the motion information encoded in multiple adjacent frames. The developed model generates multiple channels of information from the input frames, and the final feature representation combines information from all channels. To further boost the performance, we propose regularizing the outputs with high-level features and combining the predictions of a variety of different models. We apply the developed models to recognize human actions in the real-world environment of airport surveillance videos, and they achieve superior performance in comparison to baseline methods.

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Cites

Show all 13 references

Research works printed but not linked: 46

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Cited by

Items 101–124 of 124

Metadata

Authors
Ji, S., Xu, W., Yang, M., & Yu, K.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2013-01
Volume
35
Issue
1
Pages
221-231
ISSN
  • 0162-8828 (ISSN-L)
  • 2160-9292
DOI
10.1109/tpami.2012.59
OpenAlex
W237546731
Cited by
6,235
References
13
FWCI
81.96
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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