Contextual anomaly detection in crowded surveillance scenes

Michael Leach, Ed Sparks, Neil Robertson

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)
285 Downloads (Pure)


This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability.
Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour.
Keywords: behavior analysis, visual surveillance, security, context
Original languageEnglish
JournalPattern Recognition Letters
Early online date7 Dec 2013
Publication statusPublished - 2014


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