A Survey of Datasets for Human Gesture Recognition

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

RGB-D-based action recognition datasets: A survey (2016, background): “Review papers categorizing and summarizing their characteristics are available to help researchers in evaluating their algorithms [5, 6, 7].”
Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, background): “Several earlier surveys were also published to review methods to recognize human poses, motions, gestures, and activities [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], as well as their applications [49], [50].”
A Low Power, Fully Event-Based Gesture Recognition System (2017, background): “A plethora of hand gesture datasets have been created in recent years, as thoroughly reviewed in [37].”
Vision-based human activity recognition: a survey (2020, background): “Ontheother hand, reviews of vision-based hand gesture recognition methods were presented in [33, 34, 45, 64, 119, 145, 153, 227].”
Using Convolutional 3D Neural Networks for User-independent continuous gesture recognition (2016, background): “ConGD consists of 47,933 gesture samples belonging to 249 gesture classes, making it the largest user-independent dataset [27] surpassing the ChaLearn 2014 Gesture Recognition Dataset [22] which has 13,858 samples and the DeviSign Chinese Sign Language Dataset which has 24,000 samples [28].”