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• W2892333621 abstract "Dynamic hand tracking and gesture recognition is a hard task since there are many joints on the fingers and each joint owns many degrees of freedom. Besides, object occlusion is also a thorny issue in finger tracking and posture recognition. Therefore, we propose a robust and customized system for realtime hand tracking and gesture recognition under occlusion environment. First, we model the angles between hand keypoints and encode their relative coordinate vectors, then we introduce GAN to generate raw discrete sequence dataset. Secondly we propose a time series forecasting method in the prediction of defined hand keypoint location. Finally, we define a sliding window matching method to complete gesture recognition. We analyze 11 kinds of typical gestures and show how to perform gesture recognition with the proposed method. Our work can reach state of the art results and contribute to build a framework to implement customized gesture recognition task." @default.
• W2892333621 created "2018-09-27" @default.
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• W2892333621 date "2018-09-16" @default.
• W2892333621 modified "2023-09-27" @default.
• W2892333621 title "Robust and customized methods for real-time hand gesture recognition under object-occlusion." @default.
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• W2892333621 hasPublicationYear "2018" @default.
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