ChaLearn LAP IsoGD and ConGD datasets
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WARNING: Even though this dataset includes people from different ethnical backgrounds, gender, and ethnicity, it was not designed to be balanced and/or representative of any given population.
We have built two large-scale gesture datasets: ChaLearn LAP Isolated Gesture Dataset (IsoGD) and ChaLearn LAP Continous Gesture Dataset (ConGD). The focus of the challenges is "large-scale" learning and "user independent" gesture recogniton form RGB or RGB-D videos.
Both dataset was created from the CGD 2011 dataset.
1. Isolated Gesture Recognition Challenge
Database Infomation and Format
This database includes 47933 RGB-D gesture videos (about 9G). Each RGB-D video represents one gesture only, and there are 249 gestures labels performed by 21 different individuals.
The database has been divided to three sub-datasets for the convenience of using, and these three subsets are mutually exclusive.
Metric Evaluation: Recognition Rate
Main Task:
1) isolate gesture recognition using RGB and depth videos
2) Large scale: 47749 gestures and 249 labels
3) User Independent: the uses in training set will not disappear in testing and validation set.
Baseline Method: BOW+MFSK+SVM and BOW+(MFSK+deep ID)+SVM
2. Continuous Gesture Recognition Challenge
Database Infomation and Format
This database includes 47933 RGB-D gestures in 22535 RGB-D gesture videos (about 4G). Each RGB-D video may represent one or more gestures, and there are 249 gestures labels performed by 21 different individuals.
The database has been divided to three sub-datasets for the convenience of using, and these three subsets are mutually exclusive.
Metric Evaluation: Jaccard Index
Main Task:
1) gesture spotting and recognition from continuous RGB and depth videos
2) Large scale: 47933 gestures in 22535 RGB-Depth videos, 249 labels
3) User Independent: the uses in training set will not disappear in testing and validation set.
Baseline Method: BOW+MFSK+SVM+sliding window and BOW+(MFSK+deep ID)+SVM+sliding window
To use both datasets please cite:
Jun Wan, Yibing Zhao, Shuai Zhou, Isabelle Guyon, and Sergio Escalera and Stan Z. Li, "ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition", CVPR workshop, 2016.
Apply both datasets: