1 research outputs found
Gesture Recognition in RGB Videos UsingHuman Body Keypoints and Dynamic Time Warping
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.Comment: 13 pages, 4 figures, 2 tables, RoboCup 2019 Symposiu