Crowd density estimation method and pedestrian volume statistical method based on video analysis

Abstract

The invention discloses a crowd density estimation method based on video analysis and a pedestrian volume statistical method based on the video analysis. The crowd density estimation method includes the flowing steps of (1) off-line training: manually counting crowd density data, extracting characteristics and conducting training; and (2) on-line estimating: extracting the characteristics and conducting regression prediction by utilizing trained model parameters. The pedestrian volume statistical method includes the step of setting up a robust relationship between a scene and a line-passing number of people by combing the crowd density and a micro-region pedestrian flow speed before a line is passed. Characteristics such as foregrounds, edges and gray scale co-occurrence matrixes are extracted based on a whole area to conduct crowd density estimation, problems of dense crowds, sheltering and the like can be well solved through mixing of the characteristics, and real-time crowd density estimation is achieved. In addition, on the basis of area crowd density estimation, pedestrian volume estimation is conducted through combination of the pedestrian flow speed based on an optical flow, detection and tracking of a large number of individuals under a complex environment are avoided, and two-way pedestrian volume counting of accurate robust under dense crowds is achieved.

Description

technical field [0001] The invention relates to the technical field of crowd density and people flow processing, in particular to a crowd density estimation method and a people flow statistics method based on video analysis. Background technique [0002] With the rapid increase of urban population density, many public infrastructures often usher in short-term peak flow of people, and the high degree of crowding is likely to cause various emergencies. Therefore, it is very necessary to estimate the crowd density in public infrastructure and other places, and then carry out subsequent management and coordination. In addition, real-time and reliable statistical information of people flow is also of great significance in many fields such as traffic control, business analysis, and statistics of the number of people traveling on holidays. The traditional statistical method of manual monitoring is not only time-consuming and laborious, but also cannot guarantee the statistical acc...

AI Technical Summary

Problems solved by technology

However, the existing methods basically only use the accumulated foreground pixels combined with the flow rate of people as a statistical feature to estimate the number of people crossing the line, which does not consider the occlusion problem enough, and is not suitable for the situation where the crowd is highly dense and the occlusion is serious.

[0007] In addition, the existing methods generally do not consider issues such as lighting, camera distance, angle, etc., and their robustness is poor, and crowd density estimation and traffic statistics are implemented separately, which increases the amount of calculation for video analysis

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