  Real-time moving object detection in unconstrained scenes is a difficult task
due to dynamic background, changing foreground appearance and limited
computational resource. In this paper, an optical flow based moving object
detection framework is proposed to address this problem. We utilize homography
matrixes to online construct a background model in the form of optical flow.
When judging out moving foregrounds from scenes, a dual-mode judge mechanism is
designed to heighten the system's adaptation to challenging situations. In
experiment part, two evaluation metrics are redefined for more properly
reflecting the performance of methods. We quantitatively and qualitatively
validate the effectiveness and feasibility of our method with videos in various
scene conditions. The experimental results show that our method adapts itself
to different situations and outperforms the state-of-the-art methods,
indicating the advantages of optical flow based methods.
