Ricerc@Sapienza

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
and neural background subtraction method for real-time moving object detection in video sequences acquired
by PTZ cameras. Initially, the method performs a spatio-temporal tracking of the sets of moving keypoints to
recognize the foreground areas and to establish the background. Subsequently, it adopts a neural background
subtraction to accomplish a foreground detection, in these areas, able to manage bootstrapping and gradual
illumination changes. Experimental results on two well-known public datasets and comparisons with different
key works of the current state-of-the-art demonstrate the remarkable results of the proposed method.