Crowd density analysis method based on statistical characteristics

Abstract

The invention discloses a method for analyzing crowd density base on statistical characteristics. The method comprises the following steps: video input and frame extraction are carried out; the mosaic image difference MID characteristics are extracted from the video-frequency frame sequence, and the subtle movements in the crowd are detected; the uniform distribution of the sequence time of the mosaic image difference MID characteristics is checked; the geometric correction is performed to the crowd and the scene with obvious perspective phenomenon, and a contribution factor of each picture element to the crowd density is obtained on the image plane; and the weighting process is performed to the crowd space area, so as to obtain the crowd density. Compared with the prior method, the method has no need of the reference background, also has no need of the background modeling and can self-adapt change of either morning or evening light, the algorithm is quite robust, and the application is convenient; the mathematical model is simple and effective, the spatial distribution and the size of the crowd can be accurately located, and the vivacity is strong; the calculation amount is small, and the method is suitable for real-time visual monitoring. The invention can be widely applied to the monitoring and the management of the public places with detained crowd density such as the public transportation, the subway, the square and the like.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to a robust crowd density analysis method based on statistical features. Background technique [0002] With the rapid development of computer technology and digital electronic technology, visual monitoring technology has been more and more widely used. Traditional visual surveillance systems can only provide video capture and storage functions, requiring monitoring personnel to watch the display in the monitoring room to find abnormalities, which not only increases the cost of monitoring, but also causes visual fatigue caused by long-term viewing of the display. The vigilance of the system is reduced, so that the monitoring system cannot play its due role at some critical moments. At present, intelligent visual surveillance technology is on the rise, and is getting more and more attention from the media. Intelligent visual monitoring technology is to let the computer r...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem in the prior art of how to estimate the number or density of the crowd after locating the position and size of the crowd, the

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