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Besides, we develop a robust user motion recognition method for landmark identification, without requiring noisy barometers.
Besides, we develop a more robust user motion recognition method for landmark identification.
Section 3 presents the user motion recognition method for landmark identification.
We introduce conventional techniques in Section 2, describe our motion recognition method in Section 3, show results of several experiments in Section 4, and conclude in Section 5.
In this paper, we present a climbing motion recognition method using anatomical information obtained by parsing a climber's body area into its constituent anatomical parts.
Therefore, we propose a climbing motion recognition method using anatomical information obtained by classifying body parts using the climber's body area and skeletal information.
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The climbing motion recognition program for the proposed method was installed in the client.
Focusing on the combination of dynamic projection and interactive filtering visualization methods, AR devices in combination with motion recognition tools might solve a significant scaling problem especially for multidimensional representations that comes to this area from the field of Architecture.
This paper proposes a method for 3D whole-body motion recovery and motion recognition from a sequence of occluded monocular camera images based on statistical inference using a motion database.
Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data.
In this paper, we propose a method of parsing a climber's body area into body parts for climbing motion recognition.
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