Urban traffic scene vehicle detection method based on robust mixed Gaussian model

A hybrid Gaussian model, urban traffic technology, applied in the field of vehicle detection in complex urban traffic scenarios, and can solve problems such as traffic congestion

Active Publication Date: 2016-09-28
SOUTHEAST UNIV
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Problems solved by technology

[0002] As an important part of the intelligent transportation system and smart city, in recent years, the intelligentization of urban traffic has received more attention. The traffic density in urban traffic is high, the traffic congestion is serious, and the road users are

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  • Urban traffic scene vehicle detection method based on robust mixed Gaussian model
  • Urban traffic scene vehicle detection method based on robust mixed Gaussian model
  • Urban traffic scene vehicle detection method based on robust mixed Gaussian model

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[0041] Below in conjunction with specific implementation scheme, this technical scheme is further described:

[0042] A vehicle detection method in an urban traffic scene based on a robust mixed Gaussian model, comprising the following steps:

[0043] Step 1: Use the traditional mixed Gaussian model method to train the model with a learning rate based on the number of frames, and quickly obtain an ideal urban traffic scene background model:

[0044] Using a Gaussian mixture distribution to model the pixel value of each pixel over time, the pixel value of each pixel over time at position (x, y) can be expressed as {X 1 ,...,X t},X i =I(x,y,i). Here I(x,y,i) represents the gray value or color value at the position (x,y) of the i-th frame, i∈[1,t], each The pixels are modeled by a mixed Gaussian model composed of K (k is generally 3-5) Gaussian distributions, namely

[0045] f ( X t ) ...

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Abstract

The invention discloses an urban traffic scene vehicle detection method based on a robust mixed Gaussian model, comprising the following steps: (1) collecting an urban traffic scene video in real time, and quickly getting an ideal urban traffic scene background model; (2) introducing an image counter, an image foreground detection counter, a background confidence image counter, an image update logo and the traffic status at each pixel of an image, and setting corresponding initial values; (3) judging the traffic status at each pixel of an image in the current scene; (4) judging whether the confidence of each pixel of a background model is updated; (5) judging whether the background model of each pixel is updated; (6) during background updating, updating the background model with an adaptive learning rate according to the traffic status in the current scene; and (7) carrying out urban traffic scene foreground detection. The method is used to realize vehicle counting, vehicle model classification, vehicle tracking and traffic parameter acquisition so as to realize intelligent management of tollgate video data.

Description

technical field [0001] The patent of the present invention relates to the field of intelligent transportation research, especially the vehicle detection method in complex urban traffic scenes. Background technique [0002] As an important part of the intelligent transportation system and smart city, in recent years, the intelligentization of urban traffic has received more attention. The traffic density in urban traffic is high, the traffic congestion is serious, and the road users are diverse. From the complex urban traffic Getting the moving foreground from the background is important for urban traffic and urban public safety, yet finding a general and robust method for foreground detection and segmentation of urban traffic vehicles remains an open challenge. [0003] Background subtraction technology is an efficient method for detecting moving foreground from static camera video sequences, and the performance of background subtraction is determined by the scene background...

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Application Information

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IPC IPC(8): G08G1/01G06K9/00G06K9/62
CPCG08G1/0133G06V20/52G06F18/214
Inventor 赵池航张运胜陈爱伟齐行知
Owner SOUTHEAST UNIV
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