Method for estimating number of persons based on deep learning

A deep learning and headcount technology, applied in the field of digital images, can solve the problems of large sample requirements, high complexity, and long training time, and achieve the effects of improving estimation accuracy, reducing network complexity, and reducing training time

Active Publication Date: 2018-02-02
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

However, the current deep learning algorithms mostly use multi-column convolutional neural networ

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  • Method for estimating number of persons based on deep learning
  • Method for estimating number of persons based on deep learning
  • Method for estimating number of persons based on deep learning

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[0019] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the implementation methods and accompanying drawings.

[0020] The invention discloses a single column convolutional neural network based on 10 convolutional layers and 2 pooling layers, referred to as Crowd-CNN, which simplifies the existing deep learning network structure and realizes the estimation of the number of people in an image. see figure 1 , the specific implementation steps of the present invention are as follows:

[0021] Step 1. Build a deep neural network and train:

[0022] Step 1-1 prepares training data: for the Crowd-CNN network structure of the present invention, in this specific embodiment, the database UCSD, Shanghaitech PartA and Shanghaitech PartB commonly used in the field of people counting are used, and the label information (ground truth) of the sample is an imag...

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Abstract

The invention discloses a method for estimating the number of persons based on deep learning, and belongs to the population density estimation based on deep learning. According to the invention, the method comprises the steps: employing a single-column convolution neural network based on a convolution layer and a pooling layer; learning the population features through the training of a large number of samples, estimating the population density map of an input image, carrying out the integration of the density map, and achieving the estimation of the number of persons on the image. Compared with other conventional deep learning algorithm, the convolution neural network employed in the invention is simple in structure, is low in complexity, is short in training time, and is higher in estimation precision.

Description

technical field [0001] The invention belongs to the technical field of digital images, and in particular relates to crowd density estimation based on deep learning. Background technique [0002] With the rapid development of science and technology and the continuous improvement of economic level, people's living needs are getting higher and higher, which has promoted the rapid development of artificial intelligence. At present, artificial intelligence technology has been gradually applied to various fields, including intelligent driving and intelligent monitoring. , security and so on. The estimation of the number of people through video images has important application value in the fields of intelligent monitoring and security. In large public places, such as large-scale event sites, railway stations and other places, the number of people can be estimated in time through images, which is helpful for timely evacuation of overcrowded crowds and prevents stampedes. Wait for t...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/08
CPCG06N3/084G06V20/53G06F18/24
Inventor 解梅秦方李佩伦苏星霖
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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