Data-Driven Foreground Object Detection from a Non-stationary Camera

In this paper, we propose a data-driven foreground object detection technique which can detect foreground objects from a moving camera. We propose to build a data-driven consensus foreground object template (CFOT) and then detect the foreground object region in each frame. The proposed foreground object detection technique is equipped with the following functions: (1) the ability to detect the foreground object captured by a fast moving camera ; (2) the ability to detect a low contrast (spatially/temporally) foreground object; and (3) the ability to detect a foreground object from a dynamic background. There are three contributions of our method: (1) a newly proposed data-driven foreground region decision process for generating the CFOT has been shown robust and efficient; (2) a foreground object probability is proposed for properly dealing with the imperfect initial foreground region estimations; and (3) a CFOT is generated for precise foreground object detection.