  Gesture recognition opens up new ways for humans to intuitively interact with
machines. Especially for service robots, gestures can be a valuable addition to
the means of communication to, for example, draw the robot's attention to
someone or something. Extracting a gesture from video data and classifying it
is a challenging task and a variety of approaches have been proposed throughout
the years. This paper presents a method for gesture recognition in RGB videos
using OpenPose to extract the pose of a person and Dynamic Time Warping (DTW)
in conjunction with One-Nearest-Neighbor (1NN) for time-series classification.
The main features of this approach are the independence of any specific
hardware and high flexibility, because new gestures can be added to the
classifier by adding only a few examples of it. We utilize the robustness of
the Deep Learning-based OpenPose framework while avoiding the data-intensive
task of training a neural network ourselves. We demonstrate the classification
performance of our method using a public dataset.
