Open AccessAnomaly detection and localisation in the crowd scenes using a block‐based social force model
Author(s) -
Ji QingGe,
Chi Rui,
Lu ZheMing
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2016.0044
Subject(s) - block (permutation group theory) , anomaly detection , artificial intelligence , computer science , pixel , gaussian , mixture model , pattern recognition (psychology) , computer vision , gaussian network model , anomaly (physics) , mathematics , physics , geometry , condensed matter physics , quantum mechanics
A novel approach to detect and localise anomalous events in crowed scenes by processing surveillance videos is introduced in this study. Unusual events are those that significantly differ from current dominated behaviours. The proposed approach both detects pixel‐level and block‐level anomalies. In pixel level, Gaussian mixture models are used to detect abnormalities. Block‐level detection segments the crowd into blocks according to pedestrian detection, and then anomalies are spotted and localised with a social force model. Experimental results using the USCD datasets Ped1 and Ped2 show that the proposed method performs favourably against state‐of‐the‐art methods.