Image information-based client flow volume identification statistic method

Abstract

The invention relates to an image information-based client flow volume identification statistic method. The method is an improved self-adapting movement target detecting method. After movement targets existing in a target identification area is detected, the image sequence of the existing movement targets is processed according to the following steps of firstly, predicting the search areas of target images on the basis of Kalman filtration; secondly, matching the targets according to an improved cost function, creating a target chain, and determining corresponding relation among target images; finally, according to association matching relation of the targets between each frame of image, determining whether the targets enter the identification area or depart from the identification area, or are still or are continuously moved in / out of the target identification area, so that the aim of client flow volume identification statistics is fulfilled. The method has the characteristics of high real-time property, high identification rate, full statistic data, low hardware requirement and the like.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a method for identifying and counting passenger flow based on image information. Background technique [0002] Passenger flow has always been an indispensable and important data for management and decision-making in public places such as shopping malls, airports, bus stations, and subway stations. With the development of my country's economy and technology, the demand for passenger flow statistics in many industries is also growing. The traditional manual statistical method is time-consuming and laborious, and the timeliness is not strong. The follow-up data processing is cumbersome and cannot provide real-time statistical data. Therefore, it is of great significance to develop a real-time passenger flow intelligent statistical system. [0003] The traditional infrared photoelectric detection passenger flow statistics system is simple in principle and widely used, but h...

AI Technical Summary

Problems solved by technology

The traditional manual statistical method is time-consuming and laborious, and the timeliness is not strong. The follow-up data processing is cumbersome and cannot provide real-time statistical data. Therefore, it is of great significance to develop a real-time passenger flow intelligent statistical system.

[0003] The traditional infrared photoelectric detection passenger flow statistics system is simple in principle and widely used, but has obvious defects: the active type is greatly affected by natural conditions such as temperature and light, and has poor reliability; the passive type has strong reliability, but is expensive. And limited by the venue

This method is simple to implement, but it is easy to be confused with other non-human moving targets, which affects the accuracy of statistics

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