Crowd counting method for real scene

A real-world, crowd-counting technology, applied in neural learning methods, computing, computer components, etc., can solve the problems of large memory space, uneven distribution of people, and many model parameters, and achieve easy-to-implement effects.

Inactive Publication Date: 2020-10-09
EAST CHINA NORMAL UNIV
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  • Abstract
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  • Claims
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AI Technical Summary

Problems solved by technology

Like any other computer vision problem, crowd analysis faces many challenges, such as occlusions, high clutter, uneven distribution of people, uneven lighting, appearance, scale, and perspective variations within and between scenes, etc., making the problem It is extremely complicated, which will lead

Method used

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  • Crowd counting method for real scene
  • Crowd counting method for real scene

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Embodiment 1

[0018] See attached figure 1 In the present invention, the real crowd density map is generated by the geometric Gaussian adaptive kernel function and input to the convolution operation of the local receptive field, and the feature maps of different scales are adaptively fused and then input into the deep neural network model, and the obtained estimated density map is passed through Regression solving and integration algorithms get accurate crowd numbers, and the specific operations are as follows:

[0019] (1) Obtaining the real density map through the geometric Gaussian adaptive kernel function

[0020] First you need to convert the image with labeled human heads into a crowd density map, if at pixel x i There is a human head on it, then it is expressed as δ(x-x i ). Therefore, an image labeled with n heads can be expressed as a function by the following formula:

[0021]

[0022] To transform this into a continuous density function, one can pass the Gaussian kernel G ...

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Abstract

The invention discloses a crowd counting method for a real scene. The method is characterized by adopting a geometric Gaussian adaptive kernel function to generate a real crowd density map as a real value to guide training; inputting the crowd image into the convolution operation of the local receptive field, fusing the generated adaptive feature maps of different scales, inputting the fused feature maps into a deep neural network model, and obtaining the number of people in the scene through regression solution and an integral method according to the obtained estimated density map. Compared with the prior art, the method is simple and convenient, strong in real-time performance and high in accuracy of people counting, can quickly obtain a high-quality crowd density map by adopting a lightweight network structure, does not lose too much statistical precision, occupies a small memory space of model parameters, and is particularly suitable for various scenes with high real-time performance requirements, such as attendance systems, crowd monitoring systems and the like.

Description

technical field [0001] The invention relates to the technical field of crowd image counting, in particular to a crowd counting method in a real scene based on density map estimation. Background technique [0002] The goal of the crowd counting task is to estimate the number of people in a surveillance video or photo. Crowd counting from a single photo is of vital importance in areas such as traffic management, disaster warning, and public area management. In addition, this technology can also be applied to other research fields such as cell microscopy and vehicle counting. Crowd counting is an important research area in the field of computer vision and intelligent video surveillance due to its wide range of applications, and it has also received a lot of attention from both the computer vision research community and private companies in recent years. Like any other computer vision problem, crowd analysis faces many challenges, such as occlusions, high clutter, uneven distr...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/53G06V10/454G06N3/045G06F18/241
Inventor 杨静石晓雯
Owner EAST CHINA NORMAL UNIV
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