Crowd density estimation method

A crowd density and crowd technology, applied in computing, computer components, instruments, etc., can solve problems such as blurred density maps, overestimation, and large differences in density maps, and achieve good robustness, good crowd density estimation characteristics, and improved Effects on Problems with Dissimilar Density Map Distributions

Pending Publication Date: 2020-03-31
SHANGHAI INSTITUTE OF TECHNOLOGY
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Problems solved by technology

For the deep learning algorithm, due to the influence of the loss function, the density map learned by the network is relatively blurred, which is quite different from the real corresponding density map, and it may also cause the problem of overestimation

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[0027] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0028] Such as figure 1 and 2 As shown, the present invention provides a method for estimating crowd density, which includes in the training session:

[0029] Step S1, according to the corresponding crowd position coordinates given by the crowd image data set with a density greater than the preset threshold, construct the corresponding truth map, train the AlexNet network for image classification, and train the feature network of the fusion attention mechanism in the case of intensive training and Train the feature network of fusion hole convolution in the case of sparseness;

[0030] In step S2, the AlexNet network, the feature network fused with attention mechanism and the feature network fused with atrous convolution are buil...

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Abstract

The invention provides a crowd density estimation method. The method comprises the steps: enabling a crowd image data set to be divided into a dense image data set and a sparse image data set throughemploying an AlexNet network, and respectively transmitting the dense image data set and the sparse image data set to corresponding feature extraction networks according to the difference of density features of the two types of images, thereby obtaining better and more effective crowd density estimation features. The method is used for estimating the number of crowds in the high-density crowd picture, and can prevent accidents caused by excessive crowding of the crowds. The invention relates to a combined network crowd density estimation algorithm. Crowd density estimation is carried out on crowd density and sparseness. According to the method, effective crowd density estimation features can be better provided, the problem that density map distribution is not similar can be solved, and themethod has good robustness.

Description

technical field [0001] The invention relates to a crowd density estimation method. Background technique [0002] In recent years, video image analysis based on convolutional neural networks has become a hot topic in the field of machine vision, and its applications are very extensive. Among them, crowd density estimation is an important branch. Crowd density estimation refers to the process of outputting the corresponding crowd density map through the convolutional neural network for high-density crowd pictures, and summing all the pixels in the density map to obtain the total number of people. [0003] At present, in addition to traditional crowd counting algorithms, many crowd counting algorithms based on deep learning have been continuously proposed. For the deep learning algorithm, due to the influence of the loss function, the density map learned by the network is relatively blurred, which is quite different from the real corresponding density map, and may also cause ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/53G06F18/241G06F18/253
Inventor 王莉赵怀林汪涛
Owner SHANGHAI INSTITUTE OF TECHNOLOGY
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