# CHALEARN Gesture Challenge — Data

来源：https://www.kaggle.com/competitions/GestureChallenge/data

采集时间：2026-10-24T22:17:42Z

Data releases
December 7, 2011
Sample data released. See the
[README](http://www.causality.inf.ethz.ch/Gesture/sample_kinect_data/README.txt)
file for release notes. You may download all the batches
develxx.zip
xx=1 to 20 and
validxx.zip
xx=1 to 20 individually or download the two large archives
devel_all_files_combined.7z
and
valid_all_files_combined.7z
. The videos are in a quasi-lossless AVI format. See also our
[data mirrors](http://gesture.chalearn.org/data/sample-cgd2011)
if you have download problems.
January 7. 2012
Entire dataset released. See the
[README](http://www.causality.inf.ethz.ch/Gesture/CGD2011/README.txt)
file for release notes. You may download all the batches develxx, xx=1 to 480 in grouped archives
devel01-40, devel41-80 ... devel441-480.
The videos are in a quasi-lossless AVI format. We also provide
[videos compressed in a lossy AVI format](https://sites.google.com/a/chalearn.org/gesturechallenge/data/cgd2011)
.
March 7, 2012
Final evaluation data for round 1 available
[from various mirrors](https://gesture.chalearn.org/data/final-evaluation)
in encrypted zip archives (the decryption key will be made available on April 7). The data are available is lossy compressed AVI format (
final-1-20
) and in quasi-lossless AVi format (
final01, final02, ... final20
).
The depth normalization information may be downloaded from Depth_info_final.txt
April 7, 2012
The decyption key for the final evaluation data is:
4lkc221:
Use Winzip to decompress/decrypt the data (a free trial version is available from the Winzip website).
Data subsets
Development data:
Fully labeled data, which can be used for developing your system. The data are broken up into batches devel01, devel02, ... etc.
Validation data:
Data on which you can get immediate feed-back on the public learderboard. The data are broken up into batches valid01, valid02,... You get only one labeled example of each gesture in each batch, the rest of the labels should be predicted.
Final evaluation data:
Data the will be made available shortly before the end of the challenge to perform the final evaluation. It will be similar to the validation data, but the gesture vocabularies will be different.
File format
Each batch
develxx
or
validxx
contains:
RGB videos called
M_yy.avi
, where yy runs from 1 to 47.
The corresponding depth images are called
K_yy.avi.
Training label files
develxx_train.csv
or
validxx_train.csv
Test label files (for development data only)
develxx_test.csv
The label files have the video number yy in the first column followed by a comma, followed by the list of labels. The gesture labels in each batch refer to gestures from different gesture vocabularies. Hence the gestures labeled 1 in devel01 and devel02 are different.
The README files provide you with a table of normalization constants that were used to format the depth data. Using the K_yy.avi files, the original depth values can be restored (approximately) as follows: (1) Average the R,G, and B values to get a value v; (2) Perform v/255*(MaxDepth-MinDepth)+MinDepth.
The validation data was manually checked. Although human classification performances are of the order of 0.02 (2% error), there are very few real errors in the data. The human errors are mostly due to inattention. After examination of the truth values of the labels, about 1/1000 examples were found to be real errors. However, we point out a few errors in training data for the validation set:
1) valid01_10: The example recorded is incorrect. The gesture played is an example of pattern 1 instead of an example of pattern 5. You may use instead valid01_37 as an example of class 5.
2) valid11 contains classes that are impossible to visually distinguish: class 2 = class 4 and class 5 = class 10. We suggest that you always predict "4" for the examples of class "2" and you always predict "10" for the examples of class "5" because those are the most abundant classes.
Sample code
We provide
[Matlab TM code](http://www.causality.inf.ethz.ch/Gesture/CGD2011/sample_code_6_1_2012.zip)
to browse though the data and process it to create a
[sample submission](http://www.kaggle.com/c/GestureChallenge/Download/sample_predict.csv)
. The data can also be viewed with most video viewers.
Citation
If you cite the dataset, please use:
"ChaLearn Gesture Dataset (CGD2011), ChaLearn, California, 2011"
[More information...](http://www.kaggle.com/c/GestureChallenge/Details/Datadescription)