ANOMALY DETECTION WITH MACHINE LEARNING ALGORITHMS IN CROWDED SCENES IN UMN ANOMALY DATASET

Density aware anomaly detection in crowded scenes

Coherent nature of crowd movement allows representing the crowd motion using sparse features. However, surveillance videos recorded at different periods of time are likely to have different crowd densities and motion characteristics. These varying scene properties necessitate use of different models for an effective representation of behaviour at different periods. In this study, a density awar...

Contextual anomaly detection in crowded surveillance scenes

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...

Anomaly Detection In Crowded Scenes Using Dense Trajectories

Abnormal crowd behavior has become a popular research topic in recent years. This is related to a rise in the need for electronic video surveillance. Many methods have been proposed to detect abnormalities, but these methods rely on optical flow or classical classification techniques. We propose to follow the general pipeline used by previous works, but upgrade several components with state-of-...

Anomaly detection with Machine learning

Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms

Anomaly detection is the practice of identifying items or events that do not conform to an expected behavior or do not correlate with other items in a dataset. It has previously been applied to areas such as intrusion detection, system health monitoring, and fraud detection in credit card transactions. In this paper, we describe a new method for detecting anomalous behavior over network perform...