Spatial–temporal convolutional neural networks for anomaly detection and localization in crowded scenes

作者：

Highlights：

• A mechanism for localizing dynamic regions in crowded scenes is proposed.

• A spatial–temporal Convolutional Neural Network is designed to automatically extract spatial–temporal features of the crowd.

• The performance of anomaly detection is improved when the analysis is concentrated on the dynamic regions only.

• The anomaly events that take place in small regions are effectively detected and localized by the spatial–temporal Convolutional Neural Network.

摘要

Highlights•A mechanism for localizing dynamic regions in crowded scenes is proposed.•A spatial–temporal Convolutional Neural Network is designed to automatically extract spatial–temporal features of the crowd.•The performance of anomaly detection is improved when the analysis is concentrated on the dynamic regions only.•The anomaly events that take place in small regions are effectively detected and localized by the spatial–temporal Convolutional Neural Network.

论文关键词：Spatial–temporal CNN,Anomaly detection,Crowded scene,Surveillance

论文评审过程：Received 7 March 2016, Revised 10 May 2016, Accepted 17 June 2016, Available online 14 July 2016, Version of Record 6 August 2016.

论文官网地址：https://doi.org/10.1016/j.image.2016.06.007