Dynamic gesture learning and identifying method based on Chebyshev neural network

Abstract

The invention discloses a dynamic gesture learning and identifying method based on a Chebyshev neural network. Chebyshev orthogonal polynomials serve as hidden-layer neuron excitation functions for constructing a multi-input multi-output three-layer feedforward neural network, and a weights direct determination method and a hidden-layer node number adaptive determination algorithm are given; a fingertip detection algorithm based on a color histogram and a fingertip tracking algorithm based on bigraph optimal matching are given for obtaining a dynamic gesture track in real time; an MIMO-CNN (multi-input multi-output Chebyshev neural network) is subjected to input output structure design and network weights learning training according to the dynamic gesture identifying requirements, and a dynamic gesture is identified by the trained MIMO-CNN. A test result shows that the MIMO-CNN can increase the network training speed and improve the network training precision, so that the dynamic gesture learning speed is increased and the dynamic gesture identifying accuracy is improved; moreover, relatively good robustness and generalization ability in the aspect of dynamic gesture identification are achieved.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to a dynamic gesture learning and recognition method based on Chebyshev neural network. Background technique [0002] Gesture recognition human-computer interaction technology mainly realizes the operation of the computer by recognizing user gestures, which can provide a more natural means of human-computer interaction [1～3] . The dynamic gesture recognition based on machine vision mainly uses the camera to track the moving target (fingertip), and then judges the interaction semantics of the gesture by calculating the correlation coefficient between the fingertip movement trajectory and the preset template [4，5] . [0003] The dynamic gesture recognition system is a relatively complex nonlinear dynamic system. It is difficult to determine the function mapping between its input (fingertip movement trajectory) and output (gesture category). To perform dynamic gesture recogniti...

AI Technical Summary

Problems solved by technology

[0006] The object of the present invention is to provide a dynamic gesture learning and recognition method based on the Chebyshev neural network, which solves the problems of slow learning speed and low recognition accuracy in existing methods for dynamic gestures

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