Ricerc@Sapienza

Moving object detection in video streams plays a key role in many computer vision applications. In particular, separation between background and foreground items represents a main prerequisite to carry out more complex tasks, such as object classification, vehicle tracking, and person re-identification. Despite the progress made in recent years, a main challenge of moving object detection still regards the management of dynamic aspects, including bootstrapping and illumination changes.

The automatic scene analysis is still a topic of great interest in computer vision due to the growing possibilities provided by the increasingly sophisticated optical cameras. The background modeling, including its initialization and its updating, is a crucial aspect that can play a main role in a wide range of application domains, such as vehicle tracking, person re-identification and object recognition.

Background modeling is a preliminary task for many computer vision applications, describing static elements of a scene and isolating foreground ones. Defining a robust background model of uncontrolled environments is a current challenge since the model must manage many issues, e.g., moving cameras, dynamic background, bootstrapping, shadows, and illumination changes.