This paper presents an approach for detecting anomalous events in videos with crowds. The main goal is to recognize patterns that might lead to an anomalous event. An anomalous event might be characterized by the deviation from the normal or usual, but not necessarily in an undesirable manner, e. g., an anomalous event might just be different from normal but not a suspicious event from the surveillance point of view. One of the main challenges of detecting such events is the difficulty to create models due to their unpredictability and their dependency on the context of the scene. Based on these challenges, we present a model that uses general concepts, such as orientation, velocity, and entropy to capture anomalies. Using such a type of information, we can define models for different cases and environments. Assuming images captured from a single static camera, we propose a novel spatiotemporal feature descriptor, called histograms of optical flow orientation and magnitude and entropy, based on optical flow information. To determine the normality or abnormality of an event, the proposed model is composed of training and test steps. In the training, we learn the normal patterns. Then, during test, events are described and if they differ significantly from the normal patterns learned, they are considered as anomalous. The experimental results demonstrate that our model can handle different situations and is able to recognize anomalous events with success. We use the well-known UCSD and Subway data sets and introduce a new data set, namely, Badminton.
