Passenger congestion detection method based on convolution neural network for urban rail transit

A convolutional neural network and urban rail transit technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as large measurement errors and poor real-time performance, reduce workload and improve safety and service quality effects

Active Publication Date: 2019-03-08
NANJING METRO GRP +3
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  • Abstract
  • Description
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AI Technical Summary

Problems solved by technology

[0006] The present invention is aimed at the problems in the prior art, and provides a method for detecting passenger congestion in urban rail transit based on a convolutional neural network, which overcomes the problems of large measurement errors and poor real-time performance in the prior art. Based on the surveillance video collected by the surveillance system, using the powerful image recognition ability of the convolutional neural network, the mixed features of the crowd image and the motion residual image are extracted by constructing a multi-level convolutional neural network, so as to more comprehensively characterize the image in the surveillance video. Passenger flow status, realize the detection of congestion level, and improve the accuracy of algorithm detection

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  • Passenger congestion detection method based on convolution neural network for urban rail transit
  • Passenger congestion detection method based on convolution neural network for urban rail transit
  • Passenger congestion detection method based on convolution neural network for urban rail transit

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Embodiment 1

[0033] Convolutional neural network-based passenger congestion detection method for urban rail transit, such as figure 1 shown, including the following steps:

[0034] S1, acquiring traffic monitoring video to be detected, preprocessing the video to be detected, segmenting and extracting motion residual images;

[0035] S11, acquiring traffic monitoring video to be detected;

[0036] S12, set the detection cycle T, divide the video to be detected into video segments with a length T according to the detection cycle T, and the first frame image of the video segment is a reference image, denoted as p 1 ;

[0037] S13, take other images in the video segment, for example take a frame of images at 1 / 3T, 2 / 3T and T in the detection unit respectively, denoted as p 2 ,p 3 ,p 4 , will p 2 ,p 3 ,p 4 Respectively with the reference image p 1 To do the difference, obtain the motion residual image of the crowd in the video segment to be detected, denoted as p 12 ,p 13 ,p 14 .

...

Embodiment 2

[0047] The difference between this embodiment and Embodiment 1 is that: in step S5 to train the convolutional neural network, the stochastic gradient descent algorithm is used to correct the parameters in the convolutional neural network, and the formula of the stochastic gradient descent method is as follows :

[0048] g(θ)=∑θx i

[0049]

[0050]

[0051] Among them, g(θ) represents the network hypothesis function, θ represents the parameter weight of the convolutional neural network, h(θ) represents the loss function, and y i Indicates the sample value of the i-th sample, m indicates the total number of algorithm iterations, σ indicates the penalty coefficient, Represents the gradient, η represents the learning rate in gradient descent;

[0052] The convolutional neural network structure parameter update method involved in this method adopts an improved stochastic gradient descent method. Compared with the traditional method, the improved stochastic gradient desce...

Embodiment 3

[0054] Step 1: Set the detection period T. In this embodiment, T=3 sec is taken as an example, and the video to be detected is divided into video segments with a length T according to the detection period T, which is hereinafter referred to as a detection unit. Take the first frame image of the detection unit as the reference image, denoted as p 1 ; Take the images at t=1s, t=2s and t=3s in the detection unit, denoted as p 2 ,p 3 ,p 4 ; put p 2 ,p 3 ,p 4 Respectively with the reference image p 1 Do the difference, get the motion residual image of the crowd in the detection unit, denoted as p 12 ,p 13 ,p 14 ; Set the reference image p 1 and the motion residual image p 12 ,p 13 ,p 14 Combined into a group as the input of the passenger congestion detection algorithm; according to the actual operation of urban rail transit, the congestion is divided into 10 levels: spacious {(0-2 level), comfortable (3-5 level), crowded (5 -8 grades), danger (9-10 grades)}, intercept ...

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Abstract

The invention discloses a method for detecting passenger congestion degree of urban rail transit based on convolution neural network. The method comprises the steps of processing the video to be detected, segmenting and extracting the motion residual image, taking the combination of original image and motion residual image as the input of convolution neural network algorithm, establishing a feature extraction block comprising at least one convolution layer and a maximum pooling layer, processing and calculating crowd state characteristics contained in the original image and the motion residualimage, and then combining the characteristics of crowd state with the characteristics of movement, building to contain at least one convolution layer, a feature fusion block of a maximum pooling layer and a full connection layer, subjecting to fusion processing, at the same time, constructing a classifier to train the convolutional neural network with the pre-fabricated training set with congestion label, so that the classifier can correctly detect the passenger congestion in the video to be measured, more comprehensive characterization of monitoring the passenger flow in the video, to achieve the detection of congestion, and improve the accuracy of the algorithm detection.

Description

[0001] Field [0002] The invention belongs to the technical field of rail transit transportation, and in particular relates to a method for detecting passenger congestion degree of urban rail transit based on a convolutional neural network. Background technique [0003] With the continuous acceleration of the urbanization process and the gradual improvement of the rail transit network pattern, urban rail transit has become the main undertaker of urban public transportation. The rapid growth of passenger flow puts forward higher requirements on the level of daily operation and management. On the one hand, in order to formulate a reasonable traffic plan and passenger flow organization plan, efficiently utilize operating resources and meet the rapidly changing travel needs of passengers on a large-scale online network, it is necessary to accurately grasp the passenger flow status and passenger flow data; on the other hand, due to rail transit The station is usually located in a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08G06Q50/30
CPCG06N3/08G06Q50/30G06V20/42G06V20/53G06V20/46G06V10/50G06N3/045G06F18/253
Inventor 张宁陈毓伟何铁军裴顺鑫黎庆王健李勇汪理孙舒淼娄永梅陈亮吴昊
Owner NANJING METRO GRP
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