Traffic flow counting method based on hybrid Gaussian model

Abstract

The invention discloses a traffic flow counting method based on a hybrid Gaussian model. The method comprises the following steps of: step 1, setting a detection area on a lane of a road monitoring area; step 2, collecting video data in the detection area and preprocessing the video data; step 3, carrying out background modeling by using a hybrid Gaussian model, and separating background pixels from original images; step 4, extracting moving targets from monitoring video images; step 5, recording track information of the moving targets so as to identify vehicle information, and marking vehicles; and step 6, tracking and counting the marked vehicles so as to calculate traffic flow. According to the method, background modeling is carried out on the detection area by using the advantages of the Gaussian hybrid model, the moving targets are extracted through the background subtraction method, multi-lane traffic flow detection is realized. The method can be suitable for complex scenes, is high in detection accuracy and good in real-time performance, and has practical value.

Description

technical field [0001] The invention relates to the technical field of security monitoring, and more specifically, the invention relates to a method for counting traffic flow based on a mixed Gaussian model. Background technique [0002] With the rapid development of the modern economy, road transport has become an important means of transport in the transport industry. In order to ensure smooth traffic and driving safety, thereby improving environmental quality, the collection of traffic information is the basis of intelligent transportation systems, and the traffic flow of highways is an important part of intelligent transportation systems, so the detection of traffic flow is particularly important. [0003] The existing traffic flow detection methods in current traffic information are mainly divided into three parts: extracting moving objects from image sequences, identifying the extracted objects and counting the traffic flow. [0004] The traditional method for extract...

AI Technical Summary

Problems solved by technology

[0004] The traditional method for extracting moving objects is the background subtraction method, which uses the weighted average method to update the background, but it is easy to detect the background exposed area as the foreground, that is, the current background still has the moving object information of the previous frame, but the moving object has already disappeared at this time. If it is not in this area, there will be a "shadow" phenomenon, and the noise processing effect is not good in complex scenes such as tree branches swinging, the adaptability to the environment is poor, and the update effect is not ideal, resulting in incomplete extraction of moving vehicles, which affects the vehicle Detection accuracy

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