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

If you use this toolbox as part of a research project, please cite 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}
}

Some dependent libraries requirements: 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

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)

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.

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