Aiming at the problem that the average recognition degree of the moving\ntarget line is low with the traditional motion target behaviour recognition\nmethod, a motion recognition method based on deep convolutional neural\nnetwork is proposed in this paper. A target model of deep convolutional\nneural network is constructed and the basic unit of the network is designed\nby using the model. By setting the unit, the returned unit is calculated into\nthe standard density diagram, and the position of the moving target is determined\nby the local maximum method to realize the behavior identification of\nthe moving target. The experimental results show that the multi-parameter\nSICNN256 model is slightly better than other model structures. The average\nrecognition rate and recognition rate of the moving target behavior recognition\nmethod based on deep convolutional neural network are higher than\nthose of the traditional method, which proves its effectiveness. Since the frequency\nof single target is higher than that of multiple recognition and there is\nno target similarity recognition, similar target error detection cannot be excluded.
Loading....