  Real-time motion detection in non-stationary scenes is a difficult task due
to dynamic background, changing foreground appearance and limited computational
resource. These challenges degrade the performance of the existing methods in
practical applications. In this paper, an optical flow based framework is
proposed to address this problem. By applying a novel strategy to utilize
optical flow, we enable our method being free of model constructing, training
or updating and can be performed efficiently. Besides, a dual judgment
mechanism with adaptive intervals and adaptive thresholds is designed to
heighten the system's adaptation to different situations. In experiment part,
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 real-time methods, indicating the advantages of our optical
flow based method.
