Prof. Corso moved to the Electrical Engineering and Computer Science department at the University of Michigan in the 8/2014. He continues his work and research group in high-level computer vision at the intersection of perception, semantics/language, and robotics. Unless you are looking for something specific, historically, here, you probably would rather go to his new page.
Jason J. Corso
Action Bank™
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  Processed Data Sets for Download
  Benchmark Results
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Action Bank™: A High-Level Representation of Activity in Video

Human motion and activity is extremely complex. Most promising recent approaches are based on low- and mid-level features (e.g., local space-time features, dense point trajectories, and dense 3D gradient histograms). In contrast, the Action Bank™ method is a new high-level representation of activity in video. In short, it embeds a video into an "action space" spanned by various action detector responses, such as walking-to-the-left, drumming-quickly, etc. The individual action detectors in our implementation of Action Bank™ are template based detectors using the action spotting work of Derpanis et al. CVPR 2010. Each individual action detector correlation video volume is transformed into a response vector by volumetric max-pooling (3-levels for a 73-dimension vector); in our library and methods there are 205 action detector templates in the bank, sampled broadly in semantic and viewpoint space. Our paper shows how a simple classifier like an SVM can use this high dimensional representation to effectively recognition realistic videos of complex human activities.

On this page, you will find downloads for our source code, already processed versions of major vision data sets, and a description about the method and the code in some more detail.

   blurred for programmatic reasons; full data used in work.

News / Updates

Code / Download:

  • The code is downloadable here. This download includes the (Python) source code for complete Action Bank™ feature representation as well as an example of svm-based classification, some tools (such as converting to a Matlab readable format), a thorough README with instructions on code use and data format, the full set of bank templates that was used in our CVPR 2012 paper, and scripts that produce the results from our CVPR 2012 paper (processed bank representations needed below).

Documentation and README files are included with the code download.

Action Bank™ is Python code. You will need a modern 2.7 Python version with Numpy/Scipy; you will also need an installation of ffmpeg and the Python shogun libraries (if you want to use our classification code included in the download). All are trivially installed via Packages on most platforms.

Finally, since computing the Action Bank™ representation can be computationally expensive, we are making the Action Bank™ version of major data sets available here. We will add future processed data sets as they are available. If you have something that you want us to process on our cluster, then contact us.

LICENSE The code is licensed for free academic and research use (non-commercial). See the LICENSE file for more details. Please contact us if any other license is needed.

Action Bank™ Versions of Data Sets

Dataset Name
Dataset Thumbnail
Raw Videos
Action Bank™ Encoded Versions

Snippet of the KTH human action dataset
Download the original videos from here.
1 Scale
Python   |   Matlab
UCF Sports
UCF Sports Action Dataset
Download the original videos from here.
2 Scales
Python   |   Matlab
UCF 50
Download the original videos from here.
1 Scale
Python   |   Matlab

Group-wise cross-validation script
Group-wise 5-fold splits for cross-validation observing video-group boundaries

Download the original videos from here.
1 Scale: e1f1g2
Python   |   Matlab
CVPR 2012 results computed from this above data; no differences in combining scales observed, but we provide the data anyway.

1 Scale: e2f1g2
Python   |   Matlab

2 Scales: e1f1g2 and e2f1g2
Python   |   Matlab

Three-Way Min-Overlap Splits Script

Benchmark Results

We have tested action bank on a variety of activity recognition data sets. See the paper for full details. Here, we include a sampling of the results.

UCF Sports

UCF 50



[1] S. Sadanand and J. J. Corso. Action bank: A high-level representation of activity in video. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2012. [ bib | code | project | .pdf ]

FAQ / Help

We try to provide some answers to frequent questions and help below in running the code and/or using the outputted banked vectors.
  1. I am running the software on a video and it hangs; what's going on?
  2. I get a RuntimeWarning on divide by zero in
  3. The classify() function in causes an AttributeError and does not work.
Question 1:     I am running the software on a video and it hangs; what's going on?
The most likely answer to this question is not that the system is hanging but that the system is processing through the method, which is relatively computationally expensive (especially in this pure python form). Here, I run through an example to give you an idea of what you should see... I am processing through the first video in the UCF50 BaseballPitch class (named: v_BaseballPitch_g01_c01.avi). This video is 320x240 and has 107 frames; it is not a big video. I copied and renamed it to /tmp/input.avi
Now, from inside of the actionbank/code folder, I call Action Bank on this single video with the command
python -s -c 2 -g 2 /tmp/input.avi /tmp/output
The -s means this is a single video and not a directory of videos. The -c 2 means use 2 cores for processing. The -g 2 means reduce the video by a factor of two before applying the bank detectors (but after featurizing).
Now, the method will run and run... Here is where it may seem like it is hanging. But, it's not. It applies each detector in parallel up to the number of cores you tell it to use. See the output of the system environment during the run.
You can probably go get a coffee now. Or go to sleep. For this one video at 320x240 and about 7 seconds, at -g 2 the bank will take about an hour to process (on my dual-core i7). See the timestamps of running the command-below. The machine stats output is below.
Once this is done, you will have two new files in the /tmp: /tmp/output_featurized.npy.gz and /tmp/output_banked.npy.gz, which are the outputs.
You can effectively discard the _featurized.npy.gz file; the bank vector is in the _banked.npy.gz file. So, you can see that it takes about two hours to process a relatively small video on a single core. If you increase the number of cores, you will see a corresponding decrease in speed (quite proportionate to the number of cores used). We, for example, process on a Linux cluster with 12-cores per machine and can process all of UCF50 in about 20 hours on 32 machines. But, this is time-consuming we agree; so, we have provided the bank output vectors above for many data sets. We are also working on speeding up action bank and will post updated code here (the current code is pure python and not the most efficient).
Question 2:     I get this runtime error when I run the code:
   actionbank/code/ RuntimeWarning: invalid value encountered in divide
   Z = V / (V.sum(axis=3))[:,:,:,np.newaxis]
This case means that there is no motion energy at all for a pixel in the video, which is quite possible for typical videos. We explicitly handle it in the subsequent lines of by checking for NAN and INF. I.e., disregard the runtime warning.
Question 3:     The classify function call in gives an AttributeError. For example, when I run, I get the following error:
 Traceback (most recent call last):
   File "", line 99, in 
   File "/home/foo/actionbank_v1_0/code/", line 263, in SVMLinear
     res = svm.classify(testfeats).get_labels()
   File "/home/foo/epd-7.3-1-rh5-x86_64/lib/python2.7/site-packages/", line 21621, in 
     __getattr__ = lambda self, name: _swig_getattr(self, SVMOcas, name)
   File "/home/foo/epd-7.3-1-rh5-x86_64/lib/python2.7/site-packages/", line 59, in _swig_getattr
     raise AttributeError(name)
 AttributeError: classify
This seems to be a change in the Shogun library interface. Our work was performed with shogun version libshogun (x86_64/v0.9.3_r4889_2010-05-27_20:52_4889). In newer versions of shogun, classify is replaced with apply. Note, we have not yet tested this in house and results may vary. We also want to point out that the module is included as an example of how to use the action bank output for classification. One can use other, preferred, classifiers or platforms, such as Random Forests or Matlab, respectively.

last updated: Tue Jul 29 10:11:58 2014; copyright jcorso