Statistic and knowledge-based moving object detection in traffic scenes
The most common approach used for vision-based traffic surveillance consists of a fast segmentation of moving visual objects (MVOs) in the scene together with an intelligent reasoning module capable of identifying, tracking and classifying the MVOs in dependency of the system goal. In this paper we describe our approach for MVOs segmentation in an unstructured traffic environment. We consider complex situations with moving people, vehicles and infrastructures that have different aspect model and motion model. In this case we define a specific approach based on background subtraction with statistic and knowledge-based background update. We show many results of real-time tracking of traffic MVOs in outdoor traffic scene such as roads, parking area intersections, and entrance with barriers.