Convolutional neural network based crowd density distribution estimation method

A convolutional neural network and crowd density technology, applied in the field of crowd density distribution estimation, can solve the problems of inability to estimate low-density areas, misjudgment in unmanned areas, and inability to obtain texture information, to overcome inefficiency and blindness, The effect of strong generalization ability and good robustness

Active Publication Date: 2017-01-11
ZHENGZHOU JINHUI COMP SYST ENG
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AI Technical Summary

Problems solved by technology

In the prior art, the crowd density estimation method based on texture features such as gray-level co-occurrence matrix is ​​to calculate the gray-level co-occurrence matrix of training samples in multiple directions of the image, and calculate its corresponding metrics such as contrast, uniformity, entropy, etc. Characterized as a feature vector, and then

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  • Convolutional neural network based crowd density distribution estimation method
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  • Convolutional neural network based crowd density distribution estimation method

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Embodiment 1

[0039] Embodiment one, see figure 1 As shown, a method for estimating crowd density distribution based on convolutional neural network specifically includes the following steps:

[0040] Step 1. Select crowd image datasets in different scenes, mark the position of the crowd in a single picture at the pixel level and count the number of people, and generate label information. The label information includes: label image and number label, where the pixels of the crowd in the label image are marked as Target gray value; Divide the original image in the image dataset and its corresponding label information into two parts, one part is used as a training sample set, and the other part is used as a test sample set. The samples in each sample set include an image, the corresponding label image and Number of people label;

[0041]Step 2. Construct the crowd segmentation full convolutional neural network and the number of people regression convolutional neural network; and use the train...

Embodiment 2

[0045] Embodiment two, see Figure 2-5 As shown, a method for estimating crowd density distribution based on convolutional neural network specifically includes the following steps:

[0046] Step 1. Select crowd image datasets in different scenes, mark the position of the crowd in a single picture at the pixel level and count the number of people, and generate label information. The label information includes: label image and number label, where the pixels of the crowd in the label image are marked as The target gray value is represented as 1 for the position pixel of a person in a single picture, and 0 for the position pixel of no crowd, and the label information is generated. For the convenience of observation, the label image in the label information is represented by a retrieval picture with a palette. Such as figure 2 As shown in: a) represents the original image, b) represents the label image; the original image and its corresponding label information in the image datas...

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Abstract

The invention relates to a convolutional neural network based crowd density distribution heat map generation method, which comprises the steps of dividing a crowd picture set into a training sample set and a test sample set, performing crowd label image segmentation by using convolutional neural network architecture, and carrying out number regression by using a convolutional neural network; correcting a density map through a multi-scale template operation, generating a crowd density distribution heat map according to the corrected density map and the regression number, and completing crowd density distribution estimation. According to the invention, deep characteristics of an image are extracted by using a powerful learning ability of the full convolutional neural network so as to perform accurate crowd segmentation, and low efficiency and blindness of density calculation of a traditional method for full image characteristics are overcome; a crowd near-far perspective effect is overcome to a certain extent through multi-scale template correction; and mapping is performed in allusion to the estimated number of people, lateral comparison can be performed on heat maps of different cameras, the method is applicable to various crowd scenes, and the crowd density distribution heat map can be acquired in real time.

Description

technical field [0001] The invention relates to the technical field of crowd density distribution estimation, in particular to a method for crowd density distribution estimation based on a convolutional neural network. Background technique [0002] Crowd density in public places is a key indicator related to passenger flow and safety issues in public places. With the development of intelligent monitoring technology, the research on the method of directly generating crowd density heat map from camera video data has important practical significance and social value. Traditional crowd density distribution methods mainly include two categories: 1. methods based on foreground segmentation, feature extraction and population regression based on motion information in video surveillance; A method for estimating people from different but complementary sources and finding the total number of people in a picture. The above traditional methods can achieve better detection results under ...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/66G06N3/08
CPCG06N3/086G06V30/194G06F18/24133
Inventor 张晨民赵慧琴彭天强
Owner ZHENGZHOU JINHUI COMP SYST ENG
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