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                     Penn Action Dataset
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Penn Action Dataset contains 2326 video sequences of 15 
different actions and human joint annotations for each 
sequence. The dataset is available for download via the
following link:

   https://www.cis.upenn.edu/~kostas/Penn_Action.tar.gz

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                          Reference
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If you use our dataset, please cite the following paper:

Weiyu Zhang, Menglong Zhu and Konstantinos Derpanis,  "From 
Actemes to Action: A Strongly-supervised Representation for
Detailed Action Understanding" International Conference on 
Computer Vision (ICCV). Dec 2013.

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                       Dataset Content
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The dataset is organized in the following format:

    /frames  ( all image sequences )
       /0001 
          000001.jpg
          000002.jpg
          ...
       /0002
        ...
    /labels  ( all annotations )
        0001.mat
        0002.mat
        ...
    /tools   ( visualization scripts )
        visualize.m
        ...

The image frames are located in the /frames folder.  All 
frames are in RGB. The resolution of the frames are within
the size of 640x480.

The annotations are in the /labels folder. The sequence
annotations include class label, coarse viewpoint, human 
body joints (2D locations and visibility), 2D bounding 
boxes, and training/testing label. Each annotation is a 
separate MATLAB .mat file under /labels.


An example annotation looks as follows in MATLAB:

    annotation = 

            action: 'tennis_serve'
              pose: 'back'
                 x: [46x13 double]
                 y: [46x13 double]
        visibility: [46x13 logical]
             train: 1
              bbox: [46x4 double]
        dimensions: [272 481 46]
           nframes: 46

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                       List of Actions
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baseball_pitch  clean_and_jerk  pull_ups  strumming_guitar  
baseball_swing  golf_swing      push_ups  tennis_forehand   
bench_press     jumping_jacks   sit_ups   tennis_serve
bowling         jump_rope       squats    

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                      Annotation Tools
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The annotation tool used in creating this dataset is also
available. Please refer to http://dreamdragon.github.io/vatic/
for more details.

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                           Contact
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Please direct any questions regarding the dataset to

Kosta Derpanis <[email protected]>
Kostas Daniilidis <[email protected]>