Anomaly detection for video surveillance in crowded environments /

Öngün, Cihan
Crowd behavior analysis and anomaly detection in crowded environments have become more important in recent years. In the literature there are two main approaches for crowd behavior analysis based on the density of the crowd. While individual analysis is more efficient for low and medium density crowds, holistic approaches which consider the crowd as a whole are more efficient for high density crowds. Crowd behavior analysis studies can be examined in 3 categories: group behavior analysis, crowd behavior analysis and anomaly detection. While group behavior analysis is based on detection and tracking of human groups, crowd behavior analysis studies considered the whole crowd in the video. These steps are generally followed by anomaly detection which is the task of detecting the events which are normally not expected in a scene. In this work, the aim is to detect behavioral anomalies in high density crowds where detection and tracking of individuals are difficult. Video scene is considered as a whole and a heat map is generated using Finite-Time Lyapunov Exponents (FTLE) based on motion changes and this heat map is divided into behavioral clusters using hierarchical clustering. Then considering the distribution of these clusters existence of anomaly is determined and abnormal cluster are detected using an adaptive threshold.


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Automated analysis of crowd behavior for anomaly detection has become an important issue to ensure the safety and security of the public spaces. Public spaces have varying people density and as such, algorithms are required to work robustly in low to high density crowds. Mainly, there are two different approaches for analyzing the crowd behavior: methods based on object tracking where individuals in a crowd are tracked and holistic methods where the crowd is analyzed as a whole. In this work, the aim is to ...
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Automated analysis of a crowd behavior using surveillance videos is an important issue for public security, as it allows detection of dangerous crowds and where they are headed. Computer vision based crowd analysis algorithms can be divided into three groups; people counting, people tracking and crowd behavior analysis. In this thesis, the behavior understanding will be used for crowd behavior analysis. In the literature, there are two types of approaches for behavior understanding problem: analyzing behavi...
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The anomaly detection task is to recognize the presence of an unusual (and potentially hazardous) state within the behaviors or activities of a computer user, system, or network with respect to some model of normal behavior which may be either hard-coded or learned from observation. An anomaly detection agent faces many learning problems including learning from streams of temporal data, learning from instances of a single class, and adaptation to a dynamically changing concept. The domain is complicated by ...
Citation Formats
C. Öngün, “Anomaly detection for video surveillance in crowded environments /,” M.S. - Master of Science, Middle East Technical University, 2014.