Gesture Recognition On Human Pose Features Of Single Images

Memmesheimer, Raphael; Mykhalchyshyna, Ivanna; Paulus, Dietrich

doi:10.1109/is.2018.8710515

Cited by 5 publications

(1 citation statement)

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“…There has also been research on performing gesture recognition on single RGB images using Convolutional Pose Machines and different supervised learning techniques [22]. The authors concluded that an extension towards using sequences rather than single images could presumably lead to significant improvements.…”

Section: Related Workmentioning

confidence: 99%

Gesture Recognition in RGB Videos Using Human Body Keypoints and Dynamic Time Warping

Schneider

Memmesheimer

Kramer

et al. 2019

Lecture Notes in Computer Science

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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.

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Section: Related Workmentioning

confidence: 99%