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 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.