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Method and system for estimating number of people in group abnormal emergencies

A technology for emergencies and crowds, applied in computing, computer components, instruments, etc., can solve problems such as the calculation of the number of people without crowds, and achieve the effects of high accuracy, strong adaptability, and high detection rate

Pending Publication Date: 2021-06-29
SHENYANG SIASUN ROBOT & AUTOMATION
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AI Technical Summary

Problems solved by technology

The above methods only judge and identify the type of abnormal events for sudden abnormal events, or only estimate the number of people in the scene, and do not calculate the number of people in the abnormal event when a group of sudden abnormal events occurs

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  • Method and system for estimating number of people in group abnormal emergencies
  • Method and system for estimating number of people in group abnormal emergencies
  • Method and system for estimating number of people in group abnormal emergencies

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Embodiment Construction

[0038] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0039] refer to figure 1 Shown is a flow chart of a preferred embodiment of the method for estimating the number of people in crowd abnormal emergencies in the present invention.

[0040] Step S1, select multiple frames of video images from the historical video images of public places as training samples, then extract the foreground image of each frame of image in the training sample, and calculate the magnitude and direction information of the optical flow vector of the foreground image, and calculate The area features and key point features of the foreground image are obtained. in particular:

[0041] Use the background difference method based on the mixed Gaussian background model to extract the foreground image of each frame of the training sample, and then use the Lucas-Kanade optical flow method to calculate the magnitude and di...

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Abstract

The invention relates to a method for estimating the number of people in group abnormal emergencies. The method comprises the following steps: selecting multiple frames of video images as training samples; extracting optical flow amplitude histogram features of each frame of image of each training sample; obtaining a support vector machine model; combining the area feature and the key point feature of the foreground image into a combined feature of the training sample; obtaining a Gaussian regression model; selecting multiple frames of video images as test samples; extracting optical flow amplitude histogram features of each frame of image of each test sample; carrying out group behavior detection by utilizing a support vector machine model, and judging whether a group abnormal emergency occurs or not; and when group abnormal emergencies occur, estimating the number of people by adopting a Gaussian regression model. The invention further relates to a system for estimating the number of people in the group abnormal emergencies. The invention can accurately detect the number of crowds in allusion to sudden abnormal dangerous events of crowds in public places, and is high in environment adaptability and high in accuracy.

Description

technical field [0001] The invention relates to a method and system for estimating the number of people in a group abnormal emergency event. Background technique [0002] In recent years, intelligent video surveillance technology has been widely used in military defense, public security, smart city, intelligent transportation and other fields, and has broad application prospects and important theoretical research value. Crowd anomaly event detection in public places is one of the hot issues in the field of computer vision, and the safety of public places is closely related to each of us. [0003] At present, most public places (such as shopping malls and railway stations) have installed video surveillance systems. However, traditional surveillance systems can only be supervised manually, and are mainly used for investigation and evidence collection through video playback, which wastes a lot of time. Human resources cannot give real-time warnings. Artificial intelligence an...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V20/52G06V10/462G06F18/2411G06F18/214
Inventor 姜楠姜子政王晓东杜振军宋健潘鑫
Owner SHENYANG SIASUN ROBOT & AUTOMATION
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