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[CVPR16] Online Detection and Classification of Dynamic Hand Gestures with Recurrent 3D CNN

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Published on Apr 11, 2016

This paper will be presented in IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) in Las Vegas, ND.

Project page:
https://research.nvidia.com/publicati...

Authors:
Pavlo Molchanov, Xiaodong Yang, Shalini Gupta, Kihwan Kim, Stephen Tyree, Jan Kautz

Abstract:
Automatic detection and classification of dynamic hand gestures in real-world systems intended for human computer interaction is challenging as: 1) there is a large diversity in how people perform gestures, making detection and classification difficult; 2) the system must work online in order to avoid noticeable lag between performing a gesture and its classification; in fact, a negative lag (classification even before the gesture is finished) is desirable, as the feedback to the user can then be truly instantaneous. In this paper, we address these challenges with a recurrent three-dimensional convolutional neural network that performs simultaneous detection and classification of dynamic hand gestures from unsegmented multi-modal input streams. We employ connectionist temporal classification to train the network to predict class labels from in-progress gestures in unsegmented input streams. In order to validate our method, we introduce a new challenging multi-modal dynamic hand gesture dataset captured with depth, color and stereo-IR sensors. On this challenging dataset, our gesture recognition system achieves an accuracy of 83.8%, outperforms competing state-of-the-art algorithms, and approaching human accuracy of 88.4%.

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