SIMCD: SIMulated crowd data for anomaly detection and prediction

作者：

Highlights：

• Developing four crowd datasets (SIMCD) that represent two major crowd scenarios.

• Proposing level of crowdedness and severity level features for model evaluation.

• Introducing a workflow for crowd dataset generation using MassMotion.

• Identifying lack of real and synthetic crowd datasets for anomaly detection.

摘要

•Developing four crowd datasets (SIMCD) that represent two major crowd scenarios.•Proposing level of crowdedness and severity level features for model evaluation.•Introducing a workflow for crowd dataset generation using MassMotion.•Identifying lack of real and synthetic crowd datasets for anomaly detection.

论文关键词：Synthetic data,Real data,Datasets,Internet of things,Crowd management,Crowd model,Simulation,Machine learning,Prediction,Anomaly detection

论文评审过程：Received 17 January 2022, Revised 25 April 2022, Accepted 28 April 2022, Available online 2 May 2022, Version of Record 8 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117475