Procedural Human Action Videos - Textual Annotations
Cesar Roberto de Souza and Adrien Gaidon and Yohann Cabon and Antonio Manuel Lopez Pena

folder phav_v2-info (318 files)
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Type: Dataset
Tags: deep learning, action recognition, procedural generation, video classification

title= {Procedural Human Action Videos - Textual Annotations},
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= {},
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 textual annotations about the contents of these videos,

   - extrinsic.txt: Extrinsic camera parameters
   - 3dpose.txt:    Actor location in camera coordinates
   - bbox2d.txt:    2D bounding boxes in screen coordinates
   - bbox3d.txt:    3D bounding boxes in world coordinates
   - colors.txt:    Color palette for pixel classes in semantic segmentation
   - instances.txt: Color palette for pixel classes in instance segmentation
   - general.txt:   Intrinsic camera parameters and other general information
   - joints.txt:    Body joint locations in screen coordinates (pose)
   - muscles.txt:   Physical properties of body muscles for the main actor
 A human-readable version of the license terms is available at:
 The full license terms are available at:
 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:

superseded= {},
terms= {By using PHAV, you agree to abide to the terms of the Creative Commons BY-NC-SA license.}

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