Perception of emotions from crowd dynamics

Abstract

Perceiving crowd emotions and understand the situation is vital to control the situations in surveillance applications. This paper introduces the evolution of methods for crowd emotion perception based on bio-inspired probabilistic models. The emotions have been perceived both in an offline and online manner from the crowd. We focus on the perception of emotion from crowd behavior and dynamics. The paper explains few probabilistic algorithms and compares these for detection of emotion of crowds and proposes a probabilistic modelling approach which is trained on data to perceive the emotions of the crowd in an area under surveillance. Emotions are defined as evolving dynamic patterns arising due to interaction of people in an environment with their relationships to the past interaction patterns. Camera sensors are used to track the motion of the individuals within a crowd scenario under observation. The data mining techniques are used to distinguish between different behaviors and events into positive and negative emotions. The results have been evaluated using simulated data from a proposed office environmen

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“…Baig et al [1] focus their work on the perception of emotion due to crowd behavior. For this end, they propose an approach based on probabilistic modeling, which is trained to perceive the emotions of people in a given area.…”

Technical Methods for Social Robots in Museum Settings: An Overview of the Literature

“…We also saw the deployment of probabilistic methods to recognize crowd emotion [40], particularly bio-inspired probabilistic models, which used information from camera sensors to recognize positive or negative emotion from individuals. The authors depicted three algorithms based on Dynamic Bayesian networks to collect local features [41] and clustered by using Self-Organized maps (SOM) [42].…”

“…Additionally, emotion detection could also be added to the system to personalize the robot's behavior. In this case, probabilistic methods, such as DBN [40] seems the best solution to detect emotions dynamically since it enables the robot to detect emotions in a crowded environment.…”

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“…The authors of [10] summarize their previous work on three bio-inspired probabilistic algorithms for perception of emotions from crowd dynamics. The first algorithm starts by partitioning the environment using an Instantaneous Topological Map (ITM) and a Dynamic Bayesian Network (DBN) is employed to model conditional interactions occurred in each subregion.…”