We present a novel framework for learning patterns of motion and sizes of objects in static
camera surveillance. The proposed method provides a new higher-level layer to the …

Trajectories of moving objects provide crucial clues for video event analysis especially in
surveillance applications. In this paper, we study the problem of detecting anomalous events …

We explore a location based approach for behavior modeling and abnormality detection. In
contrast to the conventional object based approach where an object may first be tagged …

Anomalies in many video surveillance applications have local spatio-temporal signatures,
namely, they occur over a small time window or a small spatial region. The distinguishing …

Background subtraction is the first step of many video surveillance applications. What is
considered background varies by application, and may include regular, systematic, or …

Analysis of motion patterns is an effective approach for anomaly detection and behavior
prediction. Current approaches for the analysis of motion patterns depend on known scenes …

In this paper, we present a novel anomaly detection framework which integrates motion and
appearance cues to detect abnormal objects and behaviors in video. For motion anomaly …

Many motion detection and tracking algorithms rely on the process of background
subtraction, a technique which detects changes from a model of the background scene. We …

Learning dominant motion patterns or activities from a video is an important surveillance
problem, especially in crowded environments like markets, subways etc., where tracking of …

We describe fast background scene modeling and maintenance techniques for real time
visual surveillance system for tracking people in an outdoor environment. It operates on …