README.txt

Purpose
=============
This is the code the challenge"Chalearn Looking at People 2014“
Gist: Delief Networks (Gaussian Bernoulli RBM as first layer) + Hidden Markov Networks
by Di WU: stevenwudi@gmail.com, 2015/05/27


Citation
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If you use this toolbox as part of a research project, please consider citing the corresponding paper
******************************************************************************************************
@inproceedings{wu2014leveraging,
  title={Leveraging Hierarchical Parametric Networks for Skeletal Joints Based Action Segmentation and Recognition},
  author={Wu, Di and Shao, Ling},
  booktitle={Proc. Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2014}
}
******************************************************************************************************


Dependency: Theano
-------
Some dependent libraries requirements:
(1) Theano: for deep learning tasks http://deeplearning.net/software/theano/
			Note that Wudi change some of the functionalities(Deep Belief Networks, Gaussian Bernoulli Restricted Boltzmann Machines)
			They are in the subfolder of -->TheanoDL


	
Test
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To reproduce the experimental result for test submission, there is a Python file:

Step3_SK_Test_prediction.py and there are three paths needs to be changed accordingly:

line: 60, Data folder (Test data)
data_path=os.path.join("I:\Kaggle_multimodal\Test\Test\\")  
line: 62, Predictions folder (output)
outPred=r'.\training\test'
line: 64, Submision folder (output)
outSubmision=r'.\training\test_submission'

It takes about ~20 second for each example file using only skeleton information. (I use Theano GPU model, but I reckon CPU model should almost of the same speed)

Train
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To train the network, you first need to extract the skeleton information 
1)Step1_SK_Neutral_Realtime.py--> extract neutral frames (aka., 5 frames before and after the gesture)
2) Step1_SK_Realtime.py--> extract gesture frames

3)Step1_DBN_Strucutre2.py-->Start training the networks (Step1_DBN.py specifies a smaller networks, train faster, but the larger the net is always better)

Voila, here you go.


Contact
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If you read the code and find it really hard to understand, please send feedback to: stevenwudi@gmail.com
Thank you!