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Penn Action

Action Dataset with human body joints for all frames!

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Introducing the Penn Action Dataset

Penn Action Dataset (University of Pennsylvania) 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:

Download: https://upenn.box.com/PennAction

Dataset At a Glance

Reference

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.

Dataset Content

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

List of Actions

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    

Annotation Tools

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

Contact

Please direct any questions regarding the dataset to

Menglong Zhu menglong@cis.upenn.edu

http://cis.upenn.edu/~menglong