An MCNN crowd counting method based on global density characteristics

A technology of crowd counting and density, which is applied in the field of crowd image processing, can solve the problems of loss of detail features, not fully considering the global density changes of crowd images, etc., and achieve high accuracy and robustness

Inactive Publication Date: 2019-06-28
ZHEJIANG UNIV OF TECH
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Problems solved by technology

However, the existing crowd counting methods based on deep learning do not fully consider the global density changes in the crowd image, and the con

Method used

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  • An MCNN crowd counting method based on global density characteristics
  • An MCNN crowd counting method based on global density characteristics
  • An MCNN crowd counting method based on global density characteristics

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Embodiment

[0038] Example: such as figure 1 As shown, the MCNN crowd counting method based on the combination of global density features includes the following steps:

[0039] Step 1: Use the two-dimensional Gaussian convolution kernel to convert the crowd image into a crowd density map label according to the manually marked head coordinates in the data set. An image label with N heads can be represented by the following formula (1):

[0040]

[0041] Among them, G σ (x) is a two-dimensional Gaussian convolution kernel, σ is its width parameter, δ(x-x i) is the delta function, x i Indicates the location of a human head marker point.

[0042] And, according to the real number of people in the image in the data set, the density level label of each image is generated. After reading the data set, the maximum number of people in the data set N max with minimum number N min Subtract to determine the variation range of the number of people, and specify the number M of density levels to ...

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Abstract

The invention relates to an MCNN crowd counting method based on global density characteristics. The crowd counting method can adapt to different density scenes, is capable of generating the crowd density map containing more comprehensive information, obtains the estimated number of people by integrating the crowd density map, and can avoid the influence of crowd occlusion and non-uniform crowd distribution in the crowd image on counting; and the convolutional neural network combined with global density characteristics is utilized to estimate the population density map, so that the method can be suitable for a non-uniform population distribution scene, the density map containing more comprehensive information is generated, and the population estimation result has higher accuracy and robustness.

Description

technical field [0001] The invention relates to the field of crowd image processing, in particular to an MCNN crowd counting method based on combining global density features. Background technique [0002] With the development of social economy, the scale of urban population is increasing, and the number of people gathered in public places such as stations, squares, and parks is also increasing. Large-scale crowd gathering may lead to safety accidents such as trampling. In order to better ensure personal safety, the research on crowd counting algorithms is very important. [0003] Existing crowd counting methods can be divided into three categories: direct counting methods based on object detection, indirect counting methods based on feature regression, and crowd counting methods based on deep learning. Among them, the direct counting method based on target detection uses the number of pedestrians detected from the image to count. This type of method is suitable for scenes ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
Inventor 刘志陈越沈国江
Owner ZHEJIANG UNIV OF TECH
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