Jester Dataset

Your model recognizes certain simple, single-frame gestures like a thumbs-up. But for a truly responsive, accurate system, you want your model to recognize complex gestures too, even when the differences between them are subtle. Is the person pointing to something or wagging their index finger? Is the hand cleaning the display or rubber-banding an image with two fingers? Given enough examples, your model can learn the difference.

The Jester gesture recognition dataset includes 148,092 labeled video clips of humans performing basic, pre-defined hand gestures in front of a laptop camera or webcam. It is designed for training machine learning models to recognize human hand gestures like sliding two fingers down, swiping left or right and drumming fingers.

The clips cover 27 different classes of human hand gestures, split in the ratio of 8:1:1 for training, development and testing. The dataset also includes two “no gesture” classes to help the network distinguish between specific gestures and unknown hand movements.

In the age of mobile computing, gesture/action recognition and its role in human-computer interfaces have grown in importance. The Jester video dataset allows the training of robust machine learning models to recognize human hand gestures.

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Samples from the Jester dataset

Sample classes

“Doing other things”

“Thumb Up”

“Rolling Hand Forward”

“Turning Hand Clockwise”

“Shaking Hand”

“Turning Hand Counterclockwise”

“Stop Sign”

“Zooming Out With Two Fingers”

“Swiping Left”

“Zooming Out With Two Fingers”

“Thumb Down”

Dataset details

Total number of videos

148,092

Training Set

118,562

Validation Set

14,787

Test Set (w/o labels)

14,743

Labels

27

Quality

100px

FPS

12

The Jester dataset was created with the help of more than 1,300 unique crowd actors.

Developers have successfully created classification models based on the training set and found that they perform well on the validation set. Running models on the test set, developers can achieve scores of up to 97 percent.

The video data is provided as one large TGZ archive, split into parts of 1 GB maximum. The total download size is 22.8 GB. The archive contains directories numbered from 1 to 148092. Each directory corresponds to one video and contains JPG images with a height of 100px and variable width. The JPG images were extracted from the original videos at 12 frames per seconds. The filenames of the JPGs start at 00001.jpg. The number of JPGs varies as the length of the original videos varies.

Dataset license

The Jester dataset is available for research purposes.

Data License Agreement - Research Use

Qualcomm AI Research

AI is shifting from simply seeing what is happening in front of the camera to understanding it. Data is the effective force behind these deep learning breakthroughs and is integral to the human-level performance of neural networks. Our crowd-acting approach to data collection overcomes the typical limitations of crowdsourcing, resulting in high-quality video data that is densely captioned, human-centric and diverse.

Qualcomm AI Research continues to invest in and support deep-learning research in computer vision. The publication of the Jester dataset for use by the AI research community is one of our many initiatives.

Find out more about Qualcomm AI Research.
For any questions or technical support, please contact us at [email protected]

Qualcomm AI Research is an initiative of Qualcomm Technologies, Inc.