2019/06/25 by Pascal Schneider, Schneider, Pascal, Raphael Memmesheimer +5
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Gait Recognition and Analysis #Hand Gesture Recognition Systems #Human Pose and Action Recognition #Machine Learning (cs.LG) #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.1906.12171
openalex publication_date 2019/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Gesture recognition opens up new ways for humans to intuitively interact with\nmachines. Especially for service robots, gestures can be a valuable addition to\nthe means of communication to, for example, draw the robot's attention to\nsomeone or something. Extracting a gesture from video data and classifying it\nis a challenging task and a variety of approaches have been proposed throughout\nthe years. This paper presents a method for gesture recognition in RGB videos\nusing OpenPose to extract the pose of a person and Dynamic Time Warping (DTW)\nin conjunction with One-Nearest-Neighbor (1NN) for time-series classification.\nThe main features of this approach are the independence of any specific\nhardware and high flexibility, because new gestures can be added to the\nclassifier by adding only a few examples of it. We utilize the robustness of\nthe Deep Learning-based OpenPose framework while avoiding the data-intensive\ntask of training a neural network ourselves. We demonstrate the classification\nperformance of our method using a public dataset.\n