Single image crowd counting algorithm based on multi-column convolutional neural network

A convolutional neural network and crowd counting technology, applied in computing, computer components, instruments, etc., can solve the problems of small counting scale, ineffective application of crowd information processing, and low image counting accuracy

Active Publication Date: 2016-04-27
SHANGHAI TECH UNIV
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Problems solved by technology

[0002] Existing crowd counting algorithms have many limitations such as large dependence on image segmentation technology, small counting scale, and fixed input image size. Many algorithms have low counting accuracy for images with large changes in the number of people or complex backgrounds.
Today, outdoor squares and streets are basically equipped with cameras, but crowd information processing has not been effectively applied, so accurate crowd counting or crowd density estimation algorithms are of great significance for the detection of abnormal crowd events in monitoring

Method used

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  • Single image crowd counting algorithm based on multi-column convolutional neural network
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  • Single image crowd counting algorithm based on multi-column convolutional neural network

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Embodiment Construction

[0024] In order to make the present invention more comprehensible, preferred embodiments are described in detail below with accompanying drawings.

[0025] The present invention needs to solve a given image of a crowd or a frame in a video, and then estimate the density and total number of people in each area of ​​the image.

[0026] It is known that the input image can be represented as an m×n matrix: x∈R m×n , then the actual crowd density corresponding to the input image x can be expressed as: In the formula: N is the number of people in the image, Indicates the position of each pixel in the image, x i is the position of the ith head in the image, δ( ) is the unit impact function, * is the convolution operation, with standard deviation σ i Gaussian kernel. The goal of the single image crowd counting algorithm based on multi-column convolutional neural network is to learn a crowd density from the input image x to the image (such as figure 2 The mapping function F: ...

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Abstract

The invention provides a single image crowd counting algorithm based on a multi-column convolutional neural network. The multi-column convolutional neural network has three sub-networks, the volume of a convolution kernel for each sub-network is different, each sub-network inputs the same image, feature diagrams output by the three sub-networks are linked together on a 'channel' dimension after four times of convolution and two times of pooling, and a density diagram of a crowd is obtained by 1*1 kernel convolution. The crow density obtained by the algorithm is prior to that of the existing algorithm.

Description

technical field [0001] The invention relates to an algorithm for accurate crowd counting or crowd density estimation based on a single image. Background technique [0002] Existing crowd counting algorithms have many limitations such as large dependence on image segmentation technology, small counting scale, and fixed input image size. Many algorithms have low counting accuracy for images with large changes in the number of people or images with complex backgrounds. Today, outdoor squares and streets are basically equipped with cameras, but crowd information processing has not been effectively applied. Therefore, accurate crowd counting or crowd density estimation algorithms are of great significance for the detection of abnormal crowd events in monitoring. Contents of the invention [0003] The purpose of the present invention is to provide an algorithm for accurate crowd counting or crowd density estimation based on a single image. [0004] In order to achieve the above...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06V20/53
Inventor 高盛华张营营马毅
Owner SHANGHAI TECH UNIV
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