Abnormal behavior early warning method and system for hovering event space-time big data analysis

A big data and behavioral technology, applied in the field of abnormal behavior early warning, can solve problems such as no solutions

Active Publication Date: 2016-06-15
WUHAN UNIV
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  • Abnormal behavior early warning method and system for hovering event space-time big data analysis

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Embodiment Construction

[0085] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings and embodiments.

[0086] The core idea of ​​the present invention is to identify events that are significantly different from occasional normal wandering behaviors, and according to the case analysis experience of public security officers and criminal behavior characteristics revealed by big data, the frequency, duration, and target of wandering behaviors can be automatically determined Identity equivalence, the scope of surrounding appearance and other dimensions are used for screening. For the convenience of analysis, it is further divided into two cases: single-point big data period analysis and multi-point big data collaborative analysis.

[0087] The presence of the same person in wandering behaviors that occur at different times or in different places is the most powerful evidence that constitutes a suspicious abnormal behavior. This...

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Abstract

The invention provides an abnormal behavior early warning method and system for hovering event space-time big data analysis. The method comprises the steps: according to the hovering behavior alarm information sent from intelligent monitoring cameras deployed by a monitoring point, establishing an alarm big data event base so as to carry out correlation analysis of the real time and historical big data for the alarm events for a single point and the space-time big data correlation analysis among the alarm events for a plurality of monitoring points and to deeply excavate the risk grade of the robbery criminal behavior concealed in the alarm events. For conveniently identify whether different times of hovering footprinting behaviors are made by the same people or the same group of people, the invention specifically provides a hovering pedestrian re-inspection algorithm. On the premise of not adding extra hardware cost for the monitoring system, the abnormal behavior early warning method and system for hovering event space-time big data analysis overcome the defect that single time of alarm information of the single intelligent camera is not enough to be the early warning basis, thus effectively improving the efficiency of the intelligent monitoring system.

Description

technical field [0001] The invention belongs to the technical field of spatio-temporal big data analysis, and in particular relates to a technical solution for early warning of abnormal behavior based on correlation analysis of spatio-temporal big data of wandering events. Background technique [0002] Violent robbery cases targeting banks and their business outlets, ATM machines and other financial places or cashiers, such as the Zhou Kehua case that caused a national sensation, and the Wang Haijian case that caused the "12.1" CCB bombing in Wuhan, Hubei Province in 2011. This type of robbery crime has a common feature. Before committing the crime, the criminals usually conduct on-site investigation, which is the so-called spot check. For example, in the case of Zhou Kehua, careful spot checks were carried out before each robbery to plan the timing of the crime and the escape route. , the longest stepping point process lasted 3 hours. The behavioral characteristics of crim...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/47G06F18/24147
Inventor 邵振峰蔡家骏王中元杨珂
Owner WUHAN UNIV
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