Abstract
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.
Highlights
Today, science and technology develop very quickly making new technologies and ideas easy to apply for the industry to increase productivity and work efficiency
E motion recognition problem can be solved by combining basic image processing problems, namely, object detection, recognition, and tracking. ere are many image processing algorithms that have been developed in target detection and recognition. ey are divided into two main groups, namely, advanced machine learning (ML) and deep learning (DL) techniques [2,3,4,5,6,7,8,9,10,11,12,13,14]
Since the detection and recognition algorithms require a large amount of computation and the accuracy cannot reach 100%, the object tracking techniques in gesture recognition are widely applied to ensure the continuous real-time recording of subject location and avoid interference in multisubject environments. ere are many targeting algorithms for image processing such as BOOSTING [7], MIL, kernelized correlation filter (KCF) [8], TLD, MEDIANFLOW [9], GOTURN [10], MOSSE [11], and CSRT [12, 13]
Summary
Science and technology develop very quickly making new technologies and ideas easy to apply for the industry to increase productivity and work efficiency. E goal of the algorithm is to detect gestures with real-time processing speed, minimize interference, and reduce the ability to capture unintentional gestures. Ere are many image processing algorithms that have been developed in target detection and recognition. Since the detection and recognition algorithms require a large amount of computation and the accuracy cannot reach 100%, the object tracking techniques in gesture recognition are widely applied to ensure the continuous real-time recording of subject location and avoid interference in multisubject environments. CNN (cellular neural network) technology is an analog parallel computing paradigm defined in space and found by the locality of connections between processing elements (cells or neurons) It has been introduced as a special highspeed parallel neural structure for image processing and recognition [14]. If an operation has been repeated for too long, we will start the program again
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