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

共 50 条

[1] Prediction of abnormal behaviors for intelligent video surveillance systems
Duque, Duarte
Santos, Henrique
Cortez, Paulo
2007 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING, VOLS 1 AND 2, 2007, : 362 - 367
[2] Crowd behaviors analysis and abnormal detection based on surveillance data
Cui, Jing
Liu, Weibin
Xing, Weiwei
JOURNAL OF VISUAL LANGUAGES AND COMPUTING, 2014, 25 (06): : 628 - 636
[3] Dynamic weighted aggregation for normality analysis in intelligent surveillance systems
Albusac, J.
Vallejo, D.
Castro-Schez, J. J.
Glez-Morcillo, C.
Jimenez, L.
EXPERT SYSTEMS WITH APPLICATIONS, 2014, 41 (04) : 2008 - 2022
[4] SEMANTICS-PROVIDED ENVIRONMENT VIEWS FOR NORMALITY ANALYSIS-BASED INTELLIGENT SURVEILLANCE
Lopez-Lopez, Lorenzo M.
Albusac, Javier
Jesus Castro-Schez, Jose
Jimenez-Linares, Luis
ICAART 2009: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, 2009, : 161 - +
[5] Using intelligent data analysis to detect abnormal energy consumption in buildings
Seem, John E.
ENERGY AND BUILDINGS, 2007, 39 (01) : 52 - 58
[6] A multi-agent architecture for supporting distributed normality-based intelligent surveillance
Vallejo, D.
Albusac, J.
Castro-Schez, J. J.
Glez-Morcillo, C.
Jimenez, L.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2011, 24 (02) : 325 - 340
[7] Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems
Chan, Yi-Tung
Wang, Shuenn-Jyi
Tsai, Chung-Hsien
JOURNAL OF ELECTRONIC IMAGING, 2017, 26 (05)
[8] A comprehensive survey on the applications of machine learning techniques on maritime surveillance to detect abnormal maritime vessel behaviors
Gamage, Chamali
Dinalankara, Randima
Samarabandu, Jagath
Subasinghe, Akila
WMU JOURNAL OF MARITIME AFFAIRS, 2023, 22 (04) : 447 - 477
[9] A comprehensive survey on the applications of machine learning techniques on maritime surveillance to detect abnormal maritime vessel behaviors
Chamali Gamage
Randima Dinalankara
Jagath Samarabandu
Akila Subasinghe
WMU Journal of Maritime Affairs, 2023, 22 : 447 - 477
[10] Monitoring Objects Manipulations to Detect Abnormal Behaviors
Civitarese, Gabriele
Bettini, Claudio
2017 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS), 2017,

← 1 2 3 4 5 →