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Publikācija: Affective State Based Anomaly Detection in Crowd

Publication Type Scientific article indexed in SCOPUS or WOS database
Funding for basic activity State funding for education
Defending: ,
Publication language English (en)
Title in original language Affective State Based Anomaly Detection in Crowd
Field of research 2. Engineering and technology
Sub-field of research 2.2 Electrical engineering, Electronic engineering, Information and communication engineering
Research platform None
Authors Glorija Baliniškīte
Egons Lavendelis
Māra Pudāne
Keywords Anomaly detection in crowd, dangerous anomaly detection, emotional state, person extraction from crowd, surveillance system automation.
Abstract To distinguish individuals with dangerous abnormal behaviours from the crowd, human characteristics (e.g., speed and direction of motion, interaction with other people), crowd characteristics (such as flow and density), space available to individuals, etc. must be considered. The paper proposes an approach that considers individual and crowd metrics to determine anomaly. An individual’s abnormal behaviour alone cannot indicate behaviour, which can be threatening toward other individuals, as this behaviour can also be triggered by positive emotions or events. To avoid individuals whose abnormal behaviour is potentially unrelated to aggression and is not environmentally dangerous, it is suggested to use emotional state of individuals. The aim of the proposed approach is to automate video surveillance systems by enabling them to automatically detect potentially dangerous situations.
DOI: 10.2478/acss-2019-0017
Hyperlink: https://doi.org/10.2478/acss-2019-0017 
Reference Baliniškīte, G., Lavendelis, E., Pudāne, M. Affective State Based Anomaly Detection in Crowd. Applied Computer Systems, 2019, Vol. 24, No. 2, pp. 134-140. ISSN 2255-8683. e-ISSN 2255-8691. Available from: doi:10.2478/acss-2019-0017
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