Open AccessUsing Deep Learning Models Combined with Crowd Emotion Models to Identify Abnormal Behaviors in Crowds
Author(s) -
Xiao Li,
Yu Yang,
Linyang Li,
Yiming Xu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1622/1/012051
Subject(s) - crowds , computer science , abnormality , crowd psychology , artificial intelligence , entropy (arrow of time) , feature extraction , machine learning , pattern recognition (psychology) , psychology , computer security , social psychology , physics , quantum mechanics
Aiming at the problem that the definition of crowd abnormal behavior detection is ambiguous and difficult to combine with context semantics, an algorithm using OCC human emotion model combined with crowd entropy is proposed. First calculate the crowd entropy for the crowd, and determine whether the entropy value is abnormal, if it is abnormal, further extract the optical flow OF and HOG. Then project it into two-dimensional vector data, send it to CNN for local feature extraction and combine with OCC model to achieve the description of crowd emotions. Finally, predict whether the abnormality occurs according to the judgment factor. Verified on the data set, this method shows a high accuracy.