Human interaction prediction using deep temporal features
© Springer International Publishing Switzerland 2016. Interaction prediction has a wide range of applications such as robot controlling and prevention of dangerous events. In this paper, we introduce a new method to capture deep temporal information in videos for human interaction prediction. We pro...
| Main Authors: | , , , , |
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| Format: | Conference Paper |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/69567 |
| _version_ | 1848762075278475264 |
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| author | Ke, Q. Bennamoun, M. An, Senjian Boussaid, F. Sohel, F. |
| author_facet | Ke, Q. Bennamoun, M. An, Senjian Boussaid, F. Sohel, F. |
| author_sort | Ke, Q. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © Springer International Publishing Switzerland 2016. Interaction prediction has a wide range of applications such as robot controlling and prevention of dangerous events. In this paper, we introduce a new method to capture deep temporal information in videos for human interaction prediction. We propose to use flow coding images to represent the low-level motion information in videos and extract deep temporal features using a deep convolutional neural network architecture. We tested our method on the UT-Interaction dataset and the challenging TV human interaction dataset, and demonstrated the advantages of the proposed deep temporal features based on flow coding images. The proposed method, though using only the temporal information, outperforms the state of the art methods for human interaction prediction. |
| first_indexed | 2025-11-14T10:41:48Z |
| format | Conference Paper |
| id | curtin-20.500.11937-69567 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:41:48Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-695672018-08-08T04:57:02Z Human interaction prediction using deep temporal features Ke, Q. Bennamoun, M. An, Senjian Boussaid, F. Sohel, F. © Springer International Publishing Switzerland 2016. Interaction prediction has a wide range of applications such as robot controlling and prevention of dangerous events. In this paper, we introduce a new method to capture deep temporal information in videos for human interaction prediction. We propose to use flow coding images to represent the low-level motion information in videos and extract deep temporal features using a deep convolutional neural network architecture. We tested our method on the UT-Interaction dataset and the challenging TV human interaction dataset, and demonstrated the advantages of the proposed deep temporal features based on flow coding images. The proposed method, though using only the temporal information, outperforms the state of the art methods for human interaction prediction. 2016 Conference Paper http://hdl.handle.net/20.500.11937/69567 10.1007/978-3-319-48881-3_28 restricted |
| spellingShingle | Ke, Q. Bennamoun, M. An, Senjian Boussaid, F. Sohel, F. Human interaction prediction using deep temporal features |
| title | Human interaction prediction using deep temporal features |
| title_full | Human interaction prediction using deep temporal features |
| title_fullStr | Human interaction prediction using deep temporal features |
| title_full_unstemmed | Human interaction prediction using deep temporal features |
| title_short | Human interaction prediction using deep temporal features |
| title_sort | human interaction prediction using deep temporal features |
| url | http://hdl.handle.net/20.500.11937/69567 |