In this paper we propose to use gesture recognition approaches to track a human hand in 3D space and, without the use
of special clothing or markers, be able to accurately generate code for training an industrial robot to perform the same
motion. The proposed hand tracking component includes three methods: a color-thresholding model, naïve Bayes
analysis and Support Vector Machine (SVM) to detect the human hand. Next, it performs stereo matching on the region
where the hand was detected to find relative 3D coordinates. The list of coordinates returned is expectedly noisy due to
the way the human hand can alter its apparent shape while moving, the inconsistencies in human motion and detection
failures in the cluttered environment. Therefore, the system analyzes the list of coordinates to determine a path for the
robot to move, by smoothing the data to reduce noise and looking for significant points used to determine the path the
robot will ultimately take. The proposed system was applied to pairs of videos recording the motion of a human hand in
a „real‟ environment to move the end-affector of a SCARA robot along the same path as the hand of the person in the
video. The correctness of the robot motion was determined by observers indicating that motion of the robot appeared to
match the motion of the video.
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