A multi-dimensional analysis method of traffic congestion based on deep learning

Abstract

The present invention discloses a multi-dimensional analysis method of traffic congestion degree based on deep learning, including the following steps: 1) camera preset position setting and camera calibration; 2) convolutional neural network model initialization; 3) obtaining real-time video stream; 4) Check the working status of the camera; 5) Use the convolutional neural network model to detect vehicle targets in the area of ​​interest; 6) Track vehicle targets; 7) Collect traffic parameters; 10) Report congestion events and set sleep status. The present invention adopts multi-dimensional traffic parameters such as road capacity rate and average speed to analyze the degree of traffic congestion accurately and quickly, and has good robustness and high detection accuracy.

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 and development, the number of automobiles has continued to increase, which brings convenience to transportation, but also makes traffic congestion, traffic accidents and other problems increasingly prominent. Among them, traffic congestion will not only affect people's travel efficiency, but will even lead to serious traffic accidents, endangering 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 method of traffic congestion detection is to observe the road congestion on the surveillance video, but it is impossible to observe the situation of all road sections for 24 hours a...

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 learning, and uses deep convolutional neural network (CNN) features for vehicle target detection , and use multi-dimensional traffic parameters such as road capacity and average speed to conduct accurate and rapid congestion analysis

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