TITLE:
Research on Behaviour Recognition Method for Moving Target Based on Deep Convolutional Neural Network
AUTHORS:
Jianfang Liu, Hao Zheng, Mengyi Liao
KEYWORDS:
Convolutional Neural Network, Moving Target, Recognition, Depth
JOURNAL NAME:
Journal of Computer and Communications,
Vol.8 No.9,
September
17,
2020
ABSTRACT: Aiming at the problem that the average recognition degree of the moving target line is low with the traditional motion target behaviour recognition method, a motion recognition method based on deep convolutional neural network is proposed in this paper. A target model of deep convolutional neural network is constructed and the basic unit of the network is designed by using the model. By setting the unit, the returned unit is calculated into the standard density diagram, and the position of the moving target is determined by the local maximum method to realize the behavior identification of the moving target. The experimental results show that the multi-parameter SICNN256 model is slightly better than other model structures. The average recognition rate and recognition rate of the moving target behavior recognition method based on deep convolutional neural network are higher than those of the traditional method, which proves its effectiveness. Since the frequency of single target is higher than that of multiple recognition and there is no target similarity recognition, similar target error detection cannot be excluded.