| Info hash | 52719e28efd10da65fa9ed1539792988e0f5bcf3 |
| Last mirror activity | 24d,05:21:02 ago |
| Size | 19.31GB (19,310,076,969 bytes) |
| Added | 2017-07-14 09:39:35 |
| Views | 1871 |
| Hits | 1840 |
| ID | 3782 |
| Type | multi |
| Downloaded | 74 time(s) |
| Uploaded by |  cdesouza |
| Folder | phav_v2-flow |
| Num files | 318 files [See full list] |
| Mirrors | 2 complete, 0 downloading = 2 mirror(s) total [Log in to see full list] |
phav_v2-flow (318 files)
 flow_5007.tar.bz2 |
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| flow_5008.tar.bz2 |
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| flow_5009.tar.bz2 |
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| flow_5000.tar.bz2 |
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| flow_4019.tar.bz2 |
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| flow_4014.tar.bz2 |
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| flow_3119.tar.bz2 |
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| flow_3110.tar.bz2 |
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| flow_3112.tar.bz2 |
60.41MB |
| flow_3107.tar.bz2 |
62.02MB |
| flow_3108.tar.bz2 |
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| flow_3109.tar.bz2 |
58.08MB |
| flow_3104.tar.bz2 |
69.70MB |
| flow_3105.tar.bz2 |
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| flow_3106.tar.bz2 |
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| flow_3100.tar.bz2 |
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Type: Dataset
Tags: deep learning, action recognition, procedural generation, video classification
Bibtex:
Tags: deep learning, action recognition, procedural generation, video classification
Bibtex:
@article{desouza17flow,
title= {Procedural Human Action Videos - Ground Truth Optical Flow},
keywords= {Action recognition, procedural generation, deep learning, video classification},
journal= {},
author= {Cesar Roberto de Souza and Adrien Gaidon and Yohann Cabon and Antonio Manuel Lopez Pena},
year= {2017},
url= {http://adas.cvc.uab.es/phav/},
license= {Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)},
abstract= { Procedural Human Action Videos (PHAV)
-------------------------------------
This torrent contains the Procedural Human Action Videos (PHAV) dataset. This
version of the dataset contains 39,982 videos that have been generated by a
computer. The dataset is made available under the Creative Commons BY-NC-SA
(Attribution-NonCommercial-ShareAlike 4.0 International) license. This sub-
directory contains the Horizontal and Vertical Optical Flow Ground Truth data
modality of these videos.
A human-readable version of the license terms is available at:
- https://creativecommons.org/licenses/by-nc-sa/4.0/
The full license terms are available at:
- https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
By using PHAV, you agree to abide to the terms of the Creative Commons BY-NC-SA
license. If you do not agree with the terms of this license, please contact the
dataset authors for other licensing options.
For more information, including details about how to parse the different files
included in this release, how to use them in your experiments, and how to cite
the PHAV dataset in your academical paper, please visit:
- http://adas.cvc.uab.es/phav/
},
superseded= {},
terms= {By using PHAV, you agree to abide to the terms of the Creative Commons BY-NC-SA license.}
}
