Abnormal behavior early warning method and system for spatio-temporal big data analysis of wandering events

A big data and behavior technology, applied in the field of abnormal behavior early warning, which can solve problems such as unsolved problems

Active Publication Date: 2019-01-29
WUHAN UNIV
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  • Abnormal behavior early warning method and system for spatio-temporal big data analysis of wandering events
  • Abnormal behavior early warning method and system for spatio-temporal big data analysis of wandering events
  • Abnormal behavior early warning method and system for spatio-temporal big data analysis of wandering events

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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 present invention provides an abnormal behavior early warning method and system for time-space big data analysis of loitering events. According to the loitering behavior alarm information sent by the intelligent monitoring camera deployed at the monitoring point, an alarm big data event database is established, and then single-point alarm events are carried out in real time and historically. Big data correlation analysis, spatio-temporal big data correlation analysis between multi-monitoring point alarm events, and deep excavation of the risk level of theft and robbery hidden in alarm events. In order to accurately identify whether different wandering and stepping-on behaviors are done by the same person or the same group, the present invention provides a re-detection algorithm for wandering pedestrians. On the premise of not additionally increasing the hardware cost of the monitoring system, the present invention overcomes the defect that a single alarm information of a single intelligent camera is not enough as an early warning basis, and effectively improves 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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Patent Type & Authority Patents(China)
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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