Method used for establishing semantic scene models for scene images of moving targets by utilizing computer

Abstract

The invention relates to a method used for establishing semantic scene models for scene images of moving targets by utilizing a computer. The method comprises the following steps: S1, acquiring image data of the moving targets to be processed by utilizing video data input by an image processing device, and detecting and tracking the targets; S2, learning a classifier of pedestrian and vehicles based on a coordinated training method, reducing training marked samples and fully utilizing vehicles characteristics of the targets; and classifying the targets into pedestrian and vehicles according to the classifier obtained by learning; S3, clustering the tracks of all the targets to obtain a track cluster of the targets; and S4, acquiring the distribution region of each track according to the track cluster of the targets, and obtaining the main track in the distribution region of each track as well as the entry point and out point of the track by utilizing a mean shift algorithm, thus the region with certain semantics is obtained, and application is realized.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to a semantic scene model learning method for analyzing events in video monitoring. Background technique [0002] With the development of cities and the popularity of cameras, the intelligent traffic management system based on video analysis is getting more and more attention. This intelligent traffic management system can process and analyze video data to obtain the movement pattern of traffic scenes, so as to automatically alarm some abnormal events such as violations of traffic rules, avoiding a lot of manual processing. However, due to the large variety of moving objects and complex motion patterns in traffic scenes, it is still a challenging problem to automatically learn a robust semantic scene model. [0003] Traditional semantic scene model learning methods are based on trajectory analysis. One is to use the method of target classification for trajectory analysis; th...

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Problems solved by technology

[0004] In order to solve technical problems such as errors in target tracking, large amount of calculations, and similarity that cannot reflect real measurements in the disclosed technical solution, the purpose of the present invention is to avoid errors caused by target tracking, fast calculation speed, and improved video quality. The intelligent semantic scene model learning method managed by the monitoring system in the traffic scene, for which the present invention provides a semantic scene model learning method based on target classification and trajectory clustering

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