A Survey of Video-Based Crowd Anomaly Detection in Dense Scenes
Author(s) -
Junjie Ma,
Yaping Dai,
Kaoru Hirota
Publication year - 2017
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2017.p0235
Subject(s) - computer science , anomaly detection , computer vision , artificial intelligence , key (lock) , crowd psychology , event (particle physics) , crowd simulation , scale (ratio) , population , crowds , computer security , geography , cartography , demography , quantum mechanics , sociology , physics
Population growth has made the probability of incidents at large-scale crowd events higher than ever. In the past decades, automated crowd scene analysis done by computer vision has attracted attention. However, severe occlusions and complex crowd behaviors make such analysis a challenge. As a key aspect of crowd scene analysis, a number of works dealing with dense crowd anomaly detection based on computer vision have been presented. This work is a survey of computer vision techniques for analyzing dense crowd scenes. It covers two aspects: crowd density estimation and abnormal event detection. Some problems and perspectives are discussed at the end.
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