Population gathering and dispersing detection method and device based on corner clustering weighted area

Abstract

The invention provides a population gathering and dispersing detection method and device based on a corner clustering weighted area. The method comprises the first step of obtaining a video frame image, the second step of figuring out the foreground region of a population through the mixed Gaussian background modeling method, the third step of calculating corners of the image through the Harris corner detection algorithm, the fourth step of figuring out corners of a population region in the image by using the population foreground as a mask operator, the fifth step of performing clustering on the corners by using DBSCAN, the sixth step of calculating the corner area by using the weighting method, and the seventh step of calculating the slope of the changing curve of the corner area and performing incident recognition in combination with the number of class clusters. According to the method, the corners are used for describing population distribution, so that the inaccuracy of individual segmentation of the population is avoided; by performing clustering on the corners of the population, the overall density degree of the population in the current scene can analyzed according to the number of the class clusters of the population. Event decision is performed by calculating the slope of the changing curve of the corner area, so that the decision is simple and valid. In the process of the event decision, the changes of the number of the class clusters are further added for an auxiliary, and therefore the accuracy of a decision result is improved.

Description

technical field [0001] The invention belongs to the field of crowd detection, and relates to a method and a device for detecting crowd gathering and scattering. Background technique [0002] At present, existing crowd behavior analysis systems mainly perform population counting or density counting. The few crowd detection systems also make decisions based on counting the number of people or density. However, this kind of system is easily affected by factors such as crowd occlusion when segmenting crowd individuals, and cannot guarantee the accuracy of crowd number calculation. Moreover, general people counting systems all need to have a training classifier process, which brings a lot of inconvenience to practical applications. Contents of the invention [0003] In order to solve the inaccuracy of group gathering and dispersal detection through population counting in the prior art, it is also unable to express the changing trend of the process of crowd gathering and the p...

AI Technical Summary

Problems solved by technology

However, this kind of system is easily affected by factors such as crowd occlusion in the segmentation of crowd individuals, and cannot guarantee the accuracy of crowd count calculation.

Moreover, the general population counting system requires a training classifier process, which brings a lot of inconvenience to practical applications.

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