INTELLIGENT SURVEILLANCE BASED ON NORMALITY ANALYSIS TO DETECT ABNORMAL BEHAVIORS

被引：20

作者：

Albusac, Javier ^{[1
]}

Vallejo, David ^{[1
]}

Jimenez-Linares, Luis ^{[1
]}

Castro-Schez, J. J. ^{[1
]}

Rodriguez-Benitez, Luis ^{[1
]}

机构：

[1] Univ Castilla La Mancha, Dept Informat Technol & Syst, Ciudad Real 13071, Spain

来源：

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | 2009年 / 23卷 / 07期

关键词：

Behavior analysis; environment modeling; cognitive surveillance; INTRUSION DETECTION; RECOGNITION; MOTION; SYSTEM;

D O I：

10.1142/S0218001409007612

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Intelligent surveillance refers to using Artificial Intelligence techniques in order to improve surveillance and deal with semantic information obtained from low-level security devices. In this context, the use of expert knowledge may offer a more realistic solution when dealing with the design of a surveillance system. In this work, a conceptual framework based on normality analysis to detect abnormal behaviors by means of normality concepts is presented. A normality concept specifies how a certain object should ideally behave in a concrete environment depending on such a concept. The definition of the normal path concept is studied in depth in order to analyze behaviors in an outdoor environment.

引用

页码：1223 / 1244

页数：22

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