The application of body symbols in contemporary art from the perspective of Da Vinci’s theory

Abstract

Abstract Motion recognition methods can distinguish body symbols and play an important role in real-time safety monitoring of pedestrians. In order to solve the problem of lack of data for deep learning-based action recognition models, a human motion synthesis method with hidden state initialization is proposed. Commonly used human motion synthesis methods utilize Recurrent Neural Network (RNN) to automatically generate subsequent motion sequences by taking several frames of motion data as input. In previous work, the initial hidden state of the RNN is usually set to zero or randomly initialized hidden state, resulting in a jump between the end frame of motion input and the first frame of motion synthesis, which affects the quality of the generated motion. To solve this problem, a method for estimating the initial hidden state estimation is proposed, which takes the initial hidden state as the independent variable, uses the objective function of the neural network as the optimization objective, and uses the gradient descent method to optimize the solution to obtain a suitable initial hidden state. The proposed motion model with initial hidden state estimation reduces the prediction error in the first frame by 63.51% and 6.90%, respectively, and the total error in 10 frames by 50.00% and the total errors of 10 frames were reduced by 50.00% and 4.89%, respectively. The experimental results show that the proposed motion model with initial hidden state estimation has better motion synthesis quality and motion prediction accuracy than the method without initial hidden state estimation; the proposed method improves the quality of motion synthesis by accurately estimating the first frame hidden state of the RNN human motion model, which can provide reliable data support for motion recognition models in real-time security monitoring.