A prototype application for long-time behavior modeling and abnormal events detection

In this work we present a prototype application for modelling common behaviours from long-time observations of a scene. The core of the system is based on the method proposed in (Noceti and Odone, 2012), an adaptive technique for profiling patterns of activities on temporal data - coupling a string-based representation and an unsupervised learning strategy - and detecting anomalies - i.e., dynamic events diverging with respect to the usual dynamics. We propose an engineered framework where the method is adopted to perform an online analysis over very long time intervals (weeks of activity). The behaviour models are updated to accommodate new patterns and cope with the physiological scene variations. We provide a thorough experimental assessment, to show the robustness of the application in capturing the evolution of the scene dynamics.