An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes
This addresses motion detection challenges in dynamic environments for applications like surveillance, though it appears incremental as it builds on optical flow techniques.
The paper tackles real-time motion detection in non-stationary scenes by proposing an optical flow-based framework that avoids model training and uses adaptive mechanisms, achieving superior performance over state-of-the-art real-time methods.
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.