ConGD baseline

This is the source code of the baseline method of ConGD, a large-scale continuous gesture dataset. Baseline method: MFSK features -> Sliding Window segmentation -> Kmeans -> SVM

ChaLearn Challenge and Data downloading

The ChaLearn LAP Large-scale Continuous Gesture Recognition Challenge is in progress, please feel free to participate!
You can gain access to the ChaLearn LAP ConGD dataset from http://www.cbsr.ia.ac.cn/users/jwan/database/congd.html.

Notes

This code was tested on Ubuntu 14.04 OS, with MATLAB 2013b and Python 2.7. There is a compiled MFSK binary program for Ubuntu 14.04.
Please double check the paths in code before your run it.

##Steps in detail of baseline method:

####Step 1. Extract MFSK features. For both train.txt and valid.txt use congd/batch_MFSK_feature_congd.m program to extract and save MFSK for all videos.

####Step 2. Get list of videos who only contains one gesture In this baseline method, we randomly select 10 videos for each class, and each video should only comtain one gesture. program congd/get_single_label_train_list.py can help you to selected videos and save the list.

####Step 3. Calculate Kmeans center Excute joewan_upload_V2/joewan_code/con_cal_center.m program to do all the works. Check paths before run it. It first read features of selected train videos in step2, and then uses them to calculate and save the center. This cost a lot time to process, but you can move to step 4 while waiting for it.

####Step 4. Segment validation videos and split features First, run congd/segment_videos.py to use sliding window method to segment videos. The output congd/con_list/valid_segmented.list file saved segmentation points. Then use congd/split_MFSK_features.py to split both train and validation features. This step will generate train_split.list and valid_split.list files.

####Step 5. Generate hist files Run joewan_upload_V2/joewan_code/create_hist.m with generated train_split.list and valid_split.list file to get hist files for train set and validation set.

####Step 6. Use Lib-SVM to Train SVM Model and Predict the Label of Validation Hists. First scale both train.hist and valid.hist to range [-1, 1], using commandsvm-scale. Then train svm with linear kernel type svm-train -t 0. At last, use trained model to generate labels for valid.hist.

####Step 7. Format svm prediction Run congd/joint_list.py to get formatted result for evaluation.

##Citation If you use this code in your research, please cite the following two papers:

MFSK feature:
Jun Wan, Guogong Guo, Stan Z. Li, "Explore Efficient Local Features form RGB-D Data for One-shot Learning Gesture Recognition", in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 8, pp. 1626-1639, 2016.

ConGD result:
Jun Wan, Yibing Zhao, Shuai Zhou, Isabelle Guyon, Sergio Escalera and Stan Z. Li, "ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition", CVPR workshop, 2016.

Should you have any question, please contact:
Shuai Zhou: [email protected], or
Jun Wan: [email protected]

##License

VLFeat

The kmeans algorithm we used is implemented by VLFeat toolbox.

VLFeat is distributed under the BSD license: Copyright (C) 2007-11, Andrea Vedaldi and Brian Fulkerson Copyright (C) 2012-13, The VLFeat Team All rights reserved.

ConGD Baseline

Distributed under the Apache License V2.0, please check LICENSE for further information.