A Robust and Fast Motion Segmentation Method for Video Sequences

In this paper, a robust and time-saving method for moving object detection in video sequences is proposed. Unlike methods based on complex background updating models which are computationally expensive, the proposed method can segment moving targets in real-time. It mainly includes three steps. In the first step, the background image is reconstructed by using the long-term and short-term background updating algorithms. The long-term updating algorithm detects the noisy motion regions and ghosts, while the short-term updating algorithm models the background pixel values with single Gaussian distributions, which can deal with slow lighting changes. In the second step, the shadows are removed via the color space based approach. Finally, different targets are located with the projection method. Experimental results prove that the presented method is robust to background noisy motions, shadows and scene changes, and can segment multiple objects precisely and quickly.