<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Affective State Based Anomaly Detection in Crowd

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 Public License.

{"article-title":"Affective State Based Anomaly Detection in Crowd"}

To distinguish individuals with dangerous abnormal behaviours from the crowd, human characteristics (e.g., speed and direction of motion, interaction with...

Affective State Based Anomaly Detection in Crowd

Online veröffentlicht: 20. Feb. 2020

Seitenbereich: 134 - 140

DOI: https://doi.org/10.2478/acss-2019-0017

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

© 2019 Glorija Baliniskite et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Affective State Based Anomaly Detection in Crowd

Online veröffentlicht: 20. Feb. 2020

Seitenbereich: 134 - 140

DOI: https://doi.org/10.2478/acss-2019-0017

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

© 2019 Glorija Baliniskite et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

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