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Dense crowd counting and personnel distribution estimating method based on deep learning

A dense crowd, deep learning technology, applied in neural learning methods, computing, computer components, etc., to solve the problem of people occlusion and solve the effect of excessive crowd density

Inactive Publication Date: 2017-12-22
TIANJIN UNIV
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Problems solved by technology

[0004] In order to overcome the defects of the existing technology, the present invention proposes a method for counting dense crowds and estimating personnel distribution based on deep learning, using MResNets and heat map models to realize the number of crowds under different lighting, different environments, and different resolutions Statistics and personnel distribution analysis, and has strong robustness to the problem of excessive crowd density and personnel occlusion

Method used

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  • Dense crowd counting and personnel distribution estimating method based on deep learning
  • Dense crowd counting and personnel distribution estimating method based on deep learning
  • Dense crowd counting and personnel distribution estimating method based on deep learning

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Embodiment Construction

[0029] Embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0030] Such as figure 1 As shown, it is a flow chart of the deep learning-based dense crowd counting and personnel distribution estimation method of the present invention, which is realized through the following main steps:

[0031] Step 101. Extract marked high-density crowd image data from the database. The extracted data set should have strong generalization, involving different lighting conditions, natural environment backgrounds, viewing angles, etc., and the data set should contain a large number of complex Situational scenarios, such as data such as irregular crowd gathering, ultra-high-density crowds, and severe personnel occlusion;

[0032] Step 102, preprocessing the extracted data set, the preprocessing specifically includes:

[0033] First convert the high-density crowd image data into block files, each block file contains a dict...

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Abstract

The invention discloses a dense crowd counting and personnel distribution estimating method based on deep learning, which includes the following steps: preprocessing a target crowd image to be detected; selecting an appropriate data set to train a designed deep learning network model; inputting target data to the trained network for analysis and identification; and finally, getting a thermodynamic diagram of people in the coverage area, and getting the number and distribution of people in the crowd. Through the method, the number and distribution of people in a high-density crowd can be estimated accurately, and the problem that people in a crowd are blocked due to high density is well solved. The method is applicable to video images under any lighting condition, in any environment background and of any resolution.

Description

technical field [0001] The invention relates to various fields such as pattern recognition, image processing, and machine learning, and in particular to a method for analyzing the number of people and the distribution of people in high-density crowd images based on deep learning. Background technique [0002] In the information age, artificial intelligence is a multi-field interdisciplinary subject that has gradually emerged in the past 20 years, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. Deep learning is a new research direction in the field of artificial intelligence. In recent years, breakthroughs have been made in various applications such as speech recognition, machine vision, and recommendation systems. The motivation is to build a model to simulate the neural connection structure of the human brain. When processing images, sounds, and texts, the data features are described hierarc...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/66G06N3/08
CPCG06N3/08G06V20/53G06V30/194
Inventor 刘昱穆翀刘明
Owner TIANJIN UNIV
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