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Gesture recognition and its application in human-computer interfaces have been growing increasingly popular in recent years. Although many gestures can be recognized from a single image frame, to build a responsive, accurate system, that can recognize complex gestures with subtle differences between them we need large-scale real-world video datasets. In this work, we introduce the largest collection of short clips of videos of humans performing gestures in front of the camera. The dataset has been collected with the help of over 1300 different actors in their unconstrained environments. Additionally, we present an on-going gesture recognition challenge based on our dataset and the current results. We also describe how a baseline achieving over 93% recognition accuracy can be obtained with a simple 3D convolutional neural network.
Materzyńska et al. (Tue,) studied this question.