Multi-level attention scale perception crowd counting method

A technology of crowd counting and attention, applied in computing, computer components, instruments, etc., can solve problems such as low precision, high complexity, and poor precision, and achieve the effects of overcoming incompleteness, enhancing extraction, and speeding up convergence

Pending Publication Date: 2021-08-20
SHANGHAI APPLIED TECHNOLOGIES COLLEGE
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  • Abstract
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  • Application Information

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Problems solved by technology

Method 1) using traditional methods, high complexity and poor precision;

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  • Multi-level attention scale perception crowd counting method
  • Multi-level attention scale perception crowd counting method
  • Multi-level attention scale perception crowd counting method

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[0041] In order to make the above objects, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] like figure 1 As shown, the present invention provides a multi-level precaution scale perceived population counting method, including:

[0043] A method of perceived population counting based on multi-level precaution scale, including:

[0044] S1: Get data sets and preprocessing to obtain the density map of the training set and the density map of the test set;

[0045] S2: Constructing a multi-level focus scale perceived neural network;

[0046]S3: Based on the training set, test set and multi-level attention scale perceived neural network backward, debug and training multi-level attention scale perceived neural network and test network effectiveness to obtain a good neural network;

[0047] S4: Get the camera image, enter the well-trained neural ...

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Abstract

The invention provides a multi-level attention scale perception crowd counting method, and belongs to the application of deep learning in computer vision. The method comprises the following specific steps: S1, acquiring a data set; S2, constructing a multi-level attention scale perception neural network; S3, debugging, training and testing the multi-level attention scale perception neural network; and S4, acquiring a camera image, and inputting the camera image into the trained neural network for testing to obtain a predicted density map and a predicted number of people of the image. In this way, the method can be suitable for crowd number detection in a large-scale scene, and the accuracy of a detection result is effectively improved.

Description

technical field [0001] The invention relates to a multi-level attention scale perception crowd counting method. Background technique [0002] With the acceleration of the country's urbanization and the rapid development of the urban economy, the scene of crowd gatherings has increased, and the number of tourists has increased, but at the same time there are security risks. Therefore, by designing a crowd counting method, predicting the number of people, and giving early warning to highly crowded scenes, it can help relevant personnel to carry out pre-warning and post-event decision-making for emergencies. occur. [0003] At present, there are mainly two types of crowd counting: 1) methods based on traditional methods, such as support vector machines, decision trees, etc.; 2) methods based on deep learning, such as MCNN, CSRNet and other network neural network methods. The above crowd counting methods based on deep learning all have certain limitations. Method 1) uses the ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V40/10G06V20/53G06N3/045G06F18/214
Inventor 祝鲁宁黄良军沈世晖张亚妮
Owner SHANGHAI APPLIED TECHNOLOGIES COLLEGE
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