Title :
A Robust and Fast Motion Segmentation Method for Video Sequences
Author :
Liang, Ying-hong ; Guo, Sen ; Wang, Zhi-Yan ; Xu, Xiao-wei ; Cao, Xiao-ye
Abstract :
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.
Keywords :
image motion analysis; image reconstruction; image segmentation; image sequences; video signal processing; Gaussian distributions; background pixel; fast motion segmentation method; image reconstruction; video sequences; Background noise; Computational modeling; Computer vision; Image reconstruction; Image segmentation; Motion detection; Motion segmentation; Object detection; Robustness; Video sequences; Motion segmentation; background updating; color space; shadow removing; video sequences;
Conference_Titel :
Automation and Logistics, 2007 IEEE International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-1531-1
DOI :
10.1109/ICAL.2007.4339087