Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System | Zendy

Phat Nguyen Huu | Zendy; Tan Phung Ngoc | Zendy

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Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System

Author(s) -

Phat Nguyen Huu,

Tan Phung Ngoc

Publication year - 2021

Publication title -

journal of robotics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.303

H-Index - 14

eISSN - 1687-9619

pISSN - 1687-9600

DOI - 10.1155/2021/3986497

Subject(s) - gesture , computer science , support vector machine , gesture recognition , frame (networking) , artificial intelligence , histogram , millisecond , histogram of oriented gradients , algorithm , computer vision , motion (physics) , pattern recognition (psychology) , image (mathematics) , telecommunications , physics , astronomy

In this study, we propose the gesture recognition algorithm using support vector machines (SVM) and histogram of oriented gradient (HOG). Besides, we also use the CNN model to classify gestures. We approach and select techniques of applying problem controlling for the robotic system. The goal of the algorithm is to detect gestures with real-time processing speed, minimize interference, and reduce the ability to capture unintentional gestures. Static gesture controls are used in this study including on, off, increasing, and decreasing. Besides, it uses motion gestures including turning on the status switch and increasing and decreasing the volume. Results show that the algorithm is up to 99% accuracy with a 70-millisecond execution time per frame that is suitable for industrial applications.

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