Motion Learning is a common task nowadays. Motion Learning can be done by comparing all the joints of a skeleton with those of other skeletons. A pose estimator is used to get each joint’s location. There are many pose estimators such as OpenPose, DensePose, and many more. In this thesis, OpenPose is used as the 2D pose estimator. In general, a 2D pose estimator is not enough to cover all the pose information because the 2D pose estimator only focuses from one viewing direction. To overcome this problem, a multiple-camera approach is used to get more information from multiple views. Camera calibration is needed for each camera because multiple cameras are used. From the calibration, a 3D coordinate can be obtained. Finally, scoring can be done since the pose estimator produces 3D pose information. Dynamic Time Warping (DTW) is used for the scoring calculation. DTW is an algorithm that can measure the similarity between two temporal sequences. DTW also can handle the problem if the two temporal sequences have different speeds or different number of frames.

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