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

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...

AI Technical Summary

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 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 robust method for foreground detection and segmentation of urban traffic vehicles remains an open challenge

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