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
2018-09-01
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Cambridge hand gesture datasets , dynamic hand gesture recognition , gesture recognition , LISA datasets , image motion analysis , frame motion history , three‐dimensional video volume , graph theory , feature extraction , Grassmann kernel , video signal processing , Grassmann manifold , Grassmann graph embedding discriminant analysis framework , histogram of oriented gradients , image classification , gesture classification , intelligent vehicles , auxiliary task control , intelligent transportation systems , palmprint recognition
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