Organization of Three-Dimensional Gesture Control Based on Machine Vision and Learning Technologies

Obukhov, Artem; Patutin, Kirill; Surkova, Ekaterina; Nazarova, Alexandra

doi:10.1007/978-3-031-09076-9_7

Cited by 5 publications

(2 citation statements)

References 24 publications

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“… recording a person's movement on a video camera with subsequent capture of key points of the body; the person's speed is determined by setting a fixed speed value on the treadmill [5] and using manual start/stop to record changes in walking;  simulation of the movement of a digital human model in a virtual environment, saving the animation in the form of a video sequence, and then capturing the position of all the necessary points and the current speed of the model's movement. This allows for varying the speed of movement by changing the animation speed [6].…”

Section: Methodsmentioning

confidence: 99%

Neural network algorithm for predicting human speed based on computer vision and machine learning

Obukhov,

Teselkin,

Surkova

et al. 2024

ITM Web Conf.

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The problem of increasing the accuracy of predicting human actions is an urgent task for various human-machine systems. The study examines the solution to the problem of predicting human speed using neural network algorithms, computer vision technologies, and machine learning. The formalization and software implementation of a neural network speed prediction algorithm are presented. To solve the problems of determining the current speed and predicting the upcoming positions of the human body depending on the dynamics of its movement, a comparison of various machine learning models was carried out. The RandomForestRegressor algorithm showed the best position prediction accuracy. The best determination of the current speed was demonstrated by dense multilayer neural networks. The experiment revealed that when predicting a person's position at an interval of 0.6 seconds, his speed is determined with an accuracy of more than 90%. The results obtained can be used to implement neural network algorithms for controlling human-machine systems.

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Section: Methodsmentioning

confidence: 99%

Neural network algorithm for predicting human speed based on computer vision and machine learning

Obukhov,

Teselkin,

Surkova

et al. 2024

ITM Web Conf.

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“…Software implementation of the gesture control system using the example of the virtual reality system On the basis of the resulting optimized architecture of the neural network, a gesture classification module has been developed. This module is discussed in detail in the previous work [9], the main functionality of the software includes processing of video flow, determination of gestures, coordinates of key points of the hand.…”

Section: Solving the Problem Of Optimizing The Gesture Control Systemmentioning

confidence: 99%

Development of a gesture-based system of interaction with the interface of adaptive information systems

Patutin

Surkova

Sokolinskaya

et al. 2022

J. Phys.: Conf. Ser.

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The implementation of gesture control is an actual direction in the field of organizing interaction with adaptive information systems. This study considers the development of a neural network-based gesture control system for hand recognition and gesture classification. When implementing such systems, it is necessary to ensure the maximum accuracy of gesture recognition, as well as high performance (video stream processing speed). At the first stage of the study, the task of optimizing gesture control system was posed, including analysis of the information processing algorithms used for optimization according to the criteria of classification accuracy and performance. In the course of solving the optimization problem, the structure and parameters of the neural network were determined that provide the best accuracy indicators, and the necessary changes were made in the software of the gesture control system to achieve the required level of performance. The developed software for the gestural interaction system was tested when integrating with the interface of adaptive information systems implemented on the basis of the Unity game engine. The results obtained can be used in the implementation of interfaces for interaction with virtual reality with a high level of immersion.

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Effective Gesture-Based Framework for Capturing User Input

Sri¹,

Gupta²,

Agrawal³

et al. 2023

Advances in Intelligent Systems and Computing

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Organization of Three-Dimensional Gesture Control Based on Machine Vision and Learning Technologies

Cited by 5 publications

References 24 publications

Neural network algorithm for predicting human speed based on computer vision and machine learning

Neural network algorithm for predicting human speed based on computer vision and machine learning

Development of a gesture-based system of interaction with the interface of adaptive information systems

Effective Gesture-Based Framework for Capturing User Input

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