Method for detecting road congestion in combination with visual features and convolutional neural network

A convolutional neural network and visual feature technology, applied in the field of road congestion detection that combines visual features and convolutional neural network, can solve problems such as failure to consider shadow movement prospects, general effect, and inability to fully evaluate road congestion status

Active Publication Date: 2018-02-16
FUZHOU UNIV
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AI Technical Summary

Problems solved by technology

[0005] Most of the recent traffic congestion detection technologies based on video surveillance are based on selecting appropriate visual features for detection, but the selected features cannot fully evaluate the entire road congestion state.
Furthermore, these methods also have certain limitations in the accuracy ...

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  • Method for detecting road congestion in combination with visual features and convolutional neural network
  • Method for detecting road congestion in combination with visual features and convolutional neural network
  • Method for detecting road congestion in combination with visual features and convolutional neural network

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Embodiment Construction

[0057] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0058] like figure 1 As shown, a road congestion detection method that fuses visual features and convolutional neural networks, including:

[0059] Step 1: Use the Gaussian mixture model to perform moving foreground detection and background modeling on the input image video sequence to obtain the background and preliminary moving foreground of the original image;

[0060] Step 2: Input the preliminary moving foreground set into the convolutional neural network to identify moving vehicles, exclude the moving foregrounds of other non-moving vehicles, and obtain the final moving foreground set;

[0061] Step 3: Using the final mobile foreground set to calculate the image visual features reflecting the traffic state, the image visual features include traffic density, traffic speed, traffic occupancy rate and traffic flow;

[0062] Step 4: Calculate the in...

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Abstract

The invention discloses a method for detecting road congestion in combination with visual features and convolutional neural network. The method herein includes the following steps: 1. detecting a moving foreground and establishing a background model for an image video sequence that is input, obtaining the background of an original image and an initial moving foreground; 2. Inputting the initial moving foreground set to a convolutional neural network, identifying a moving vehicle, eliminating moving foregrounds of other non-moving vehicles; 3. Using a final moving foreground set to compute theimage visual features that can reflect the state of traffic, wherein the image visual features include traffic density, traffic speed, traffic occupancy and traffic flow; 4. Computing the informationentropy of an image light flow histogram; 5. and based on the traffic density, traffic speed, traffic occupancy, traffic flow and the information entropy of the light flow histogram, determining the congestion state of a traffic road. According to the invention, the method herein combines multi-dimensional visual features and the convolutional neural network, can more accurately determine the congestion level of the road.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a road congestion detection method that fuses visual features and a convolutional neural network. Background technique [0002] With the acceleration of the urbanization process, traffic problems continue to aggravate, causing certain economic losses, leading to the paralysis of urban functions, and the problem of road congestion has caused traffic energy consumption and environmental pollution to intensify. Therefore, many literatures are dedicated to the research on intelligent transportation. The problem of road congestion has also become the focus of common concern. Road congestion detection is a key step in intelligent transportation. The detection of road congestion can keep abreast of road traffic conditions, carry out effective traffic signal dispatching, and further avoid and reduce traffic accidents. [0003] Traditional road congestion detection relies on th...

Claims

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Application Information

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IPC IPC(8): G08G1/01G08G1/017G06K9/00G06K9/46G06K9/62
CPCG08G1/0133G08G1/0175G06V20/52G06V10/40G06F18/253
Inventor 柯逍施玲凤
Owner FUZHOU UNIV
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