Affective State Based Anomaly Detection in Crowd
Glorija Baliniškīte, Egons Lavendelis, Māra Romanovska

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.

Anomaly detection in crowd, dangerous anomaly detection, emotional state, person extraction from crowd, surveillance system automation.

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

Publication language
English (en)
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