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.

Detection of moving objects is a topic of great interest in computer vision. This task represents a prerequisite
for more complex duties, such as classification and re-identification. One of the main challenges regards the
management of dynamic factors, with particular reference to bootstrapping and illumination change issues.
The recent widespread of PTZ cameras has made these issues even more complex in terms of performance due
to their composite movements (i.e., pan, tilt, and zoom). This paper proposes a combined keypoint clustering