In this study, the authors propose a novel and robust approach to control auxiliary tasks in vehicles using hand gestures. First, they create a three-dimensional video volume by appending one frame to other that captures the motion history of frames. Then, they extract features using histogram of oriented gradients on each video volume. These features are represented in the form of subspaces on Grassmann manifold. To improve the recognition accuracy, they map the data from one manifold to another manifold with the help of a Grassmann kernel. Grassmann graph embedding discriminant analysis framework is used to classify the gestures. They perform experiments on two datasets: LISA and Cambridge Hand Gesture in three different testing methods such as 1/3-subject, 2/3-subject and cross-subject. Experimental results show that their proposed model outperforms and is comparable with the state-of-the-art methods.
Framework for dynamic hand gesture recognition using Grassmann manifold for intelligent vehicles
IET Intelligent Transport Systems ; 12 , 7 ; 721-729
2018-05-21
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Grassmann manifold , video signal processing , image classification , gesture classification , image motion analysis , auxiliary task control , intelligent transportation systems , LISA datasets , gesture recognition , dynamic hand gesture recognition , graph theory , Grassmann graph embedding discriminant analysis framework , palmprint recognition , histogram of oriented gradients , frame motion history , intelligent vehicles , Cambridge hand gesture datasets , feature extraction , Grassmann kernel , three-dimensional video volume
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