Crowd evolution method based on intelligence level clustering

Abstract

Purpose The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability to complete tasks through the cooperation of many agents. In this study, the evolution of crowd intelligence is studied through the clustering method and the particle swarm optimization (PSO) algorithm. Design/methodology/approach This study proposes a crowd evolution method based on intelligence level clustering. Based on clustering, this method uses the agents’ intelligence level as the metric to cluster agents. Then, the agents evolve within the cluster on the basis of the PSO algorithm. Findings Two main simulation experiments are designed for the proposed method. First, agents are classified based on their intelligence level. Then, when evolving the agents, two different evolution centers are set. Besides, this paper uses different numbers of clusters to conduct experiments. Practical implications The experimental results show that the proposed method can effectively improve the crowd intelligence level and the cooperation ability between agents. Originality/value This paper proposes a crowd evolution method based on intelligence level clustering, which is based on the clustering method and the PSO algorithm to analyze the evolution.

An integrated multi-level feature fusion framework for crowd behaviour prediction and analysis

“…Similarly, there is a splitting of the temporal container where the size of the temporal container is defined as ∆Φ for the computation of optical motion flow. In this case, there is a re-computation of the temporal angle Φ as given (8).…”

“…The raw information that has been extracted to surveillance videos is processed for feature extraction whereas in this case, physical attributes are studied and analysed. Using this information, the various features that are needed for an automated crowd detection system is processed and passed into a machine learning model which helps in detecting, tracking and analysing objects [8].…”