Affective State Based Anomaly Detection in Crowd

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.

Identification of Anomalous Behavioral Patterns in Crowd Scenes

“…An individual's unusual behavior does not always indicates a threatening attitude toward other people because some people shows their emotions and excitement in a different way. Therefore, a system is proposed in [8] that has the ability of detecting an abnormal action of an individual automatically by taking into consideration the state of mind in which a person is behaving. Irregular LSTM models may be used to forecast the accompanying concentrations or steps of these tracks, which can be utilized in evaluating the collecting scene, using the tracks removed from the jam-packed scene as timegathering data.…”

Hybrid imaging-AI approach for handling critical situations in a fast-changing environment: preliminary study

“…Human activity recognition (HAR) has revolutionized the area of computer vision research in a wide spectrum of applications [3]. Systems based on HAR enable among others the implementation of tasks related to recognizing life threatening situations [4], preventing crime and vandalism [5,6], supervision of the sick and elderly [7], biometric face identification [8][9][10][11] and analysis and classification of all forms of human activity that may be of interest in a given situation [12][13][14][15][16][17]. To achieve full efficiency, it is required to develop optimal decision algorithms.…”