Paper Info
Title
Online Detection And Classification Of Dynamic Hand Gestures With Recurrent 3d Convolutional Neural Networks
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 before the gesture is finished) is desirable, as 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 multi-modal data. We employ connectionist temporal classification to train the network to predict class labels from inprogress gestures in unsegmented input streams. In order to validate our method, we introduce a new challenging multimodal 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 approaches human accuracy of 88.4%. Moreover, our method achieves state-of-the-art performance on SKIG and ChaLearn2014 benchmarks.
Year
DOI
Venue
2016
10.1109/CVPR.2016.456
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
Field
DocType
Volume
Computer vision,Pattern recognition,Convolutional neural network,Computer science,Gesture,Gesture recognition,Speech recognition,Artificial intelligence,Connectionism
Conference
2016
Issue
ISSN
Citations 
1
1063-6919
20
PageRank 
References 
Authors
0.63
8
6
Name
Order
Citations
PageRank
Pavlo O. Molchanov119811.96
Xiaodong Yang2109441.92
Shalini Gupta329920.42
Kihwan Kim440928.22
Stephen Tyree554838.32
Jan Kautz63615198.77