Neural Moving Object Detection by Pan-Tilt-Zoom Cameras

Automated video surveillance using video analysis and understanding technology has become an important research topic in the area of computer vision. Most cameras used in surveillance are fixed, allowing to only look at one specific view of the surveilled area. Recently, the progress in sensor technologies is leading to a growing dissemination of Pan-Tilt-Zoom (PTZ) cameras, that can dynamically modify their field of view. Since PTZ cameras are mainly used for object detection and tracking, it is important to extract moving object regions from images taken with this type of camera. However, this is a challenging task because of the dynamic background caused by camera motion. After reviewing background subtraction-based approaches to moving object detection in image sequences taken from PTZ cameras, we present a neural-based background subtraction approach where the background model automatically adapts in a self-organizing way to changes in the scene background. Experiments conducted on real image sequences demonstrate the effectiveness of the presented approach.