Multi-target traffic behavior rapid extraction method based on video recognition

Abstract

The invention provides a multi-target traffic behavior rapid extraction method based on video recognition. The method comprises the following steps: foreground target extraction: training a part of images of a to-be-processed video by using a Gaussian mixture model to obtain an initial background, training the initial background by using a visual background extraction method, further extracting acontour of a target in the foreground by using a background difference method according to the trained initial background, and performing filtering and morphological processing on the contour; contourtarget tracking: extracting feature points of each target in the to-be-processed video by using an angular point detector, and tracking each target by using a KLT optical flow algorithm to generate acorresponding image track; contour track post-processing: performing separation correction and smoothing processing on the image track of each target, and converting the image coordinates of each target in each frame of the to-be-processed video into real coordinates according to a perspective transformation principle; and result output, obtaining the corresponding traffic parameters including the speed, and judging the category to which the target belongs.

Description

technical field [0001] The invention belongs to the technical field of road traffic safety, in particular to a method for quickly extracting multi-target traffic behaviors based on video recognition. Background technique [0002] With the continuous development of urban construction, residents travel more and more, and various road traffic problems are becoming more and more prominent. At present, the study of traffic behavior and safety issues requires a large amount of measured data, and the quality of these measured data will directly affect the accuracy of the traffic behavior description model. In terms of collecting traffic data, methods based on coils, GPS or manual video selection are usually used to obtain traffic parameters. However, the frequency of data obtained by traditional manual observation or video observation is low and the error is large, which can only support relatively macroscopic statistical analysis and research; the use of coils to obtain traffic p...

AI Technical Summary

Problems solved by technology

However, this method can only detect vehicles, and the extracted target object type is single, and the tracking effect on stationary vehicles is average.

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