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A method for counting the number of people based on a convolution neural network

A convolutional neural network and people counting technology, which is applied in neural learning methods, biological neural network models, neural architectures, etc., to achieve the effect of increasing universality and reducing the accuracy of the method

Active Publication Date: 2019-01-25
YANSHAN UNIV
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Problems solved by technology

[0004] Aiming at the problems existing in the prior art, the purpose of the present invention is to provide a method for counting people based on convolutional neural network, which can effectively improve the problem of the inconsistency of image scales leading to the decline in the accuracy of counting people through the multi-scale feature fusion network. Context to obtain more contextual information to improve the universality of the people counting method in different scenarios, so as to achieve the purpose of accurate people counting

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  • A method for counting the number of people based on a convolution neural network
  • A method for counting the number of people based on a convolution neural network
  • A method for counting the number of people based on a convolution neural network

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Embodiment

[0088]figure 1 It is a flow chart of a method for counting people based on a convolutional neural network of the present invention, and the method comprises the following steps:

[0089] Step 1: Generate the actual crowd density map after processing the image sample data:

[0090] 1.1) Grayscale the image in the image sample data, set the three-channel values ​​of the color image as R, G, and B, and the grayscaled image is Gray, and the grayscale calculation formula is as follows:

[0091] Gray=R*0.299+G*0.587+B*0.114 (1)

[0092] And by dividing the data into image blocks, increasing the amount of sample data,

[0093] 1.2) Let any pixel on a single image be x, and the pixel where the head is located be x g , S is the set of pixels around the person's location, using a two-dimensional Gaussian kernel G with standard deviation σ σ and impulse function δ(x-x g ) to perform convolution* operation to obtain the crowd density map D(x) corresponding to the image, the calculatio...

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Abstract

The invention discloses a method for counting the number of people based on a convolution neural network, which relates to the field of computer vision. Firstly, the image sample data is processed andthe actual crowd density map of the sample image is generated. Then, a hierarchical context and multi-scale feature fusion network is established by extracting and processing the feature maps in thebranch network to obtain the hierarchical context information and transferring it to the backbone network, which selectively fuses the low-level and high-level feature maps in the backbone network. The network is trained by using the processed sample data. Finally, the trained model is used to count the number of people in any image. The invention effectively solves the problem that the accuracy rate drops due to the inconsistent image scale in the number of people counting task, and improves the universality of the method in different scenes.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a method for counting people based on a convolutional neural network. Background technique [0002] People counting, as one of the most basic and difficult tasks in the fields of crowd analysis, scene understanding, and video surveillance, has received extensive attention from academia and industry. People counting refers to locating crowds and estimating their size by obtaining the density map corresponding to a given image. [0003] Currently, people counting methods for a single image are mainly divided into three categories: detection-based methods, regression-based methods, and density map-based methods. Detection-based methods and regression-based methods are limited in performance due to phenomena such as severe crowd occlusion and multi-scale, while ignoring the key spatial information on the image. Therefore, in recent years, people counting tasks mostly adopt methods ba...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/06G06N3/08
CPCG06N3/06G06N3/08G06V20/53G06N3/045G06F18/24G06F18/253
Inventor 张世辉李贺桑榆
Owner YANSHAN UNIV
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