Deep Learning for Abnormal Crowd Behavior Detection:A Review
XU Tao, TIAN Chong-yang, LIU Cai-hua · DOAJ (DOAJ: Directory of Open Access Journals) · 2021
With the increasing demand of security industry,abnormal crowd behavior detection has become a hot research issue in computer vision.Abnormal crowd behavior detection aims to model and analyze the behavior of pedestrians in surveillance videos,distinguish between normal and abnormal behaviors in the crowd,and discover disasters and accidents in time.A large number of algorithms for abnormal crowd behavior detection based on deep learning are summarized in this paper.First,abnormal crowd behavior detection task and its current research situation are briefly introduced.Second,the research progress of convolutional neural networks,auto-encoder and generative adversarial networks on abnormal crowd behavior detection are discussed separately.Then,some commonly used datasets are listed,and the performance of deep learning methods on UCSD pedestrian datasets are compared and analyzed.Finally,the development difficulties of abnormal crowd behavior detection tasks are summarized,and its future research directions are discussed.