Combining Keypoint Clustering and Neural Background Subtraction for Real-time Moving Object Detection by PTZ Cameras
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