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Subway public place pedestrian volume analysis method based on machine vision

A technology of machine vision and analysis methods, which is applied to instruments, computer components, computing, etc., can solve problems such as inability to express high-level semantic features, missed detection, and poor generalization ability

Active Publication Date: 2019-11-22
WUHAN UNIV
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AI Technical Summary

Problems solved by technology

Traditional video-based target detection is realized through the change of image information between frames. Compared with static target detection, it is easy to cause missed detection.
Feature-based methods have poor generalization ability, and often extract low-level features, which cannot express high-level semantic features

Method used

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  • Subway public place pedestrian volume analysis method based on machine vision
  • Subway public place pedestrian volume analysis method based on machine vision
  • Subway public place pedestrian volume analysis method based on machine vision

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Embodiment Construction

[0043] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0044] Such as figure 1 As shown, the method for analyzing the flow of people in subway public places based on machine vision in the embodiment of the present invention comprises the following steps:

[0045] S1. Obtain the historical video stream data captured by the subway camera as a training set, from which the subway crowd training data set is extracted;

[0046] S2, load the pre-training model of the YOLOv3 network, and initialize the network weight;

[0047] S3. Obtain the open source INRIA pedestrian data set, and supplement and reorganize the real target frame labeling according to the I...

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Abstract

The invention discloses a subway public place people flow analysis method based on machine vision, and the method comprises the steps: S1, obtaining historical video flow data photographed by a subwaycamera, and extracting a subway people flow training data set; S2, loading a pre-training model of the YOLOv3 network, and initializing a network weight; S3, inputting the INRIA pedestrian data set and the subway pedestrian flow data training set into a YOLOv3 network for training to obtain a real target box label; S4, adding N times of up-sampling operation to obtain N feature maps with smallerscales, and changing the size of the input image; S5, performing network coarse training on the mixed data set, and optimizing the number, width and height of target boxes; S6, performing network finetraining on the subway people flow training data set; S7, detecting the subway people flow test data set by using the trained network model, and counting people flow; and evaluating the performance of the network model. The method is high in detection precision, the network is trained according to a strategy from coarse to fine. Boundary frame parameters are optimized, and the balance between thedetection speed and the precision is controlled.

Description

technical field [0001] The invention relates to the field of target detection, in particular to a method for analyzing the flow of people in a subway public place based on machine vision. Background technique [0002] The flow of people is an indispensable data for safety management and early warning of public places such as shopping malls and subway stations. Traditional video-based target detection is realized through the change of image information between frames. Compared with static target detection, it is easy to cause missed detection. Feature-based methods have poor generalization ability, and often extract low-level features, which cannot express high-level semantic features. In recent years, the research results of deep learning in the field of machine vision have been more and more applied to the field of target detection. It is further divided into region-based methods and regression-based methods. The regression-based method solves the problem of the balance ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/53G06V2201/07G06F18/214
Inventor 孟小亮王才群陈志伊魏冕杨一鸣王晓悦
Owner WUHAN UNIV
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