A Real-Time Background Subtraction Method With Camera Motion Compensation

Background subtraction algorithms are critical to many video recognition/analysis systems and have been studied for decades. Most of the algorithms assume that the camera is fixed. In this paper, we propose a background subtraction algorithm that works when a shaking camera is present. In this algorithm, the input frames are compensated and compared with the given reference frame to separate foreground objects from the background. The experimental results show that the proposed method outperforms the widely used Gaussian mixture model based method in both fixed camera and shaking camera scenarios with respect to accuracy, robustness, and efficiency.