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

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}
}

Dependency: Theano

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

Test

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

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.

Dataset

According to some reader recommendation, I supplement the links of the datasets used in the paper as follows:

ChaLearn Italian Gesture Recognition --> http://gesture.chalearn.org/2013-multi-modal-challenge

You should download from this dataset from Kaggle platform. https://www.kaggle.com/c/multi-modal-gesture-recognition/data

MSR Action3D --> http://research.microsoft.com/en-us/um/people/zliu/actionrecorsrc
MSRC12 --> http://research.microsoft.com/en-us/um/cambridge/projects/msrc12

(If you use the datasets, please cite the corresponding original paper. Thanks)

Contact

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