Selection of Temporal Features for Event Detection in Smart Security

Scene understanding in smart surveillance and security is one of the major fields of investigation in computer vision research and industry. The ability of a system to automatically analyze and learn the events that occur within a scene (e.g., a running person, a parking car) is conditioned by several complex aspects such as feature extraction, tracking and recognition. One of the most important aspects in the event learning process is the detection of the time interval in which an event occurs (i.e., when it starts and ends). The present paper is focused on the learning of temporal correlated events. In particular, a formalized description of the features associated with each event and the linked strategy to define the event time-line are provided. The paper also reports preliminary tests carried out on videos related to a reference outdoor environment which validate the proposed strategy.