Action recognition model construction based on multi-scale deep convolution neural network

Zhi, 刘智 Liu; Jiang-tao, 黄江涛 Huang; Xin, 冯 欣 Feng

doi:10.3788/ope.20172503.0799

Cited by 5 publications

(2 citation statements)

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“…In the formula (1), N is the number of training pictures, i X represents an input image, D represents a label density map corresponding to standard data, and F represents a density map generated by a network structure; L is the calculated loss value, and the network judges according to the magnitude of the loss value and feedbacks the relevant parameters to obtain better experimental Journal of Computer and Communications results.…”

Section: Network Basic Unit Designmentioning

confidence: 99%

“…The moving target recognition means that the computer simulates an eye to retrieve the target object of interest in the image. The recognition of the moving target is the judgment of the target category and the calibration of the location of the target, which is a basic visual processing task, but it is very difficult for the computer [1]. An image is converted into a group after it is entered into the computer.…”

Section: Introductionmentioning

confidence: 99%

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Research on Behaviour Recognition Method for Moving Target Based on Deep Convolutional Neural Network

Zheng¹,

Liu²,

Liao³

2020

JCC

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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.

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Section: Network Basic Unit Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%