Real-time global anomaly detection for crowd video surveillance using SIFT

Automated analysis of crowd behaviour using surveillance videos is an important issue for public security as it allows detection of potentially dangerous situations in crowds. Although there is a considerable amount of study in crowd behaviour analysis, the majority are limited in several ways. A few problems to mention are: limited real-time considerations, requirement of pre-set rigid anomaly rules, and high algorithm complexity. In this paper, we propose a Scale Invariant Feature Transform (SIFT) based holistic approach which is able to run in real time to detect global anomalies. Events which deviate significantly from the normal behaviour in the data set (i.e people running away) were considered as anomalies in the context of this work. The results have shown that the proposed method is well-comparable with other methods in the literature while being less complex and able to run in real time.