Crowd counting method based on coding-decoding structure multi-scale convolutional neural network

Abstract

The invention discloses a crowd counting method based on a coding-decoding structure multi-scale convolutional neural network, and the method is characterized in that the method comprises the following steps: considering the visual angle distortion of an image, and employing an adaptive Gaussian filter to calculate a true value density map of the image; adopting an encoding-decoding structure, building a multi-scale convolutional neural network model, wherein a loss function of the multi-scale convolutional neural network model comprises pixel space loss and counting error description; training and testing the built multi-scale convolutional neural network model to obtain a trained multi-scale convolutional neural network model; inputting a to-be-estimated image into the trained multi-scale convolutional neural network model, and predicting to obtain a crowd density map; and performing regression estimation on the crowd density map to obtain the number of people in the to-be-estimatedimage. According to the method, scale features and contextual information of the image can be reserved, and the output quality of the density map can be improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a crowd counting method based on an encoding-decoding structure multi-scale convolutional neural network. Background technique [0002] With the rapid development of the national economy and the continuous acceleration of urbanization, the urban population has increased sharply, and the resulting social problems have also continued to increase; for example, unsafe accidents such as stampedes caused by crowded people; The pressure of scheduling, etc. To solve the above problems, it is necessary to accurately predict the number of people in the scene, and the image can clearly and intuitively reflect the change of the crowd in the actual scene, so the estimation and counting of crowd density based on image information has important research significance. [0003] The Convolutional Neural Network (CNN) model has developed rapidly in the fields of semantic segme...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a crowd counting method based on multi-scale convolutional neural network with encoding-decoding structure, to solve the problems of multi-scale feature information loss, poor fusion and density in the crowd counting method based on multi-column convolutional neural network. Image quality is not high

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