Traffic jam degree multi-dimensional analysis method based on deep learning

A technology for traffic congestion and analysis methods, applied in the field of deep learning, which can solve problems such as robustness, slow detection speed, and low detection accuracy

Active Publication Date: 2020-04-10
ZHEJIANG UNIV OF TECH
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  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of low detection accuracy, slow detection speed, and robustness in the prior art, the present invention provides a multi-dimensional analysis method of traffic congestion based on deep le

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  • Traffic jam degree multi-dimensional analysis method based on deep learning
  • Traffic jam degree multi-dimensional analysis method based on deep learning
  • Traffic jam degree multi-dimensional analysis method based on deep learning

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[0072] 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, and do not limit the protection scope of the present invention.

[0073] like Figure 1-3 As shown, a kind of deep learning-based multi-dimensional analysis method of traffic congestion degree provided in this embodiment includes the following steps:

[0074] S1, camera preset position setting and camera calibration.

[0075] Specifically, adjust the camera to the appropriate traffic congestion analysis position, and set the current camera position as the preset position, and then capture a frame of image of the camera video stream, and perform lane lines, interest areas, and congestion level analysis areas on it calibration;

[007...

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Abstract

The invention discloses a traffic jam degree multi-dimensional analysis method based on deep learning. The method comprises the following steps: 1) setting a camera preset position and calibrating a camera; 2) initializing a convolutional neural network model; 3) acquiring a real-time video stream; 4) checking the working state of the camera; 5) performing vehicle target detection on the region ofinterest by using a convolutional neural network model; 6) tracking a vehicle target; ;7) collecting traffic jam;8) initially detecting traffic jam and making jam prediction; 9) analyzing traffic jamdegrees; 10) reporting and traffic jam event and setting sleep state. According to the invention, multi-dimensional traffic parameters such as the road accommodation rate and the average speed are adopted to carry out accurate and rapid traffic congestion degree analysis, the robustness is good, and the detection accuracy is high.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to a multi-dimensional analysis method of traffic congestion degree based on deep learning. Background technique [0002] In recent years, with the continuous advancement of urbanization development, the number of cars has continued to increase, which brings convenience to traffic, but also makes traffic congestion, traffic accidents and other problems increasingly prominent. Among them, the problem of traffic congestion will not only affect people's travel efficiency, but even lead to serious traffic accidents and endanger people's travel safety. Therefore, it is particularly important to accurately detect and analyze the degree of traffic congestion in real time. [0003] The most direct way to detect traffic congestion is to manually observe the road congestion on the surveillance video, but it is impossible to observe the situation of all road sections for 24 hours at the...

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Application Information

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IPC IPC(8): G08G1/01G06N3/04G06N3/08H04N7/18
CPCG08G1/0133G08G1/0116G06N3/08H04N7/188G06N3/045
Inventor 高飞王金超李云阳卢书芳陆佳炜张元鸣
Owner ZHEJIANG UNIV OF TECH
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