Indexed by：
Abstract：
Moving object detection under a dynamic background has been a serious challenge in real-time computer vision applications. Global motion compensation approaches, a popular existing technique, aims at compensating the moving background for moving target segmentation. However, it suffers from inaccurate global motion parameters estimation. The paper presents a moving object detection technique that combines TV-L 1 optical flow with SLIC superpixel segmentation to characterize moving objects from a dynamic background. SLIC superpixel segmentation can adhere to boundaries of objects, and thus improve the segmentation performance. TV-L 1 optical flow implemented on GPU reports competitive smooth flow field with real-time performance. Experimental results on various challenging sequences demonstrate that the proposed approach achieve impressive performance. © 2015 IEEE.
Keyword：
Reprint Author's Address：
Email：
Source ：
2015 IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2015
Year： 2015
Page： 84-89
Language： English
Cited Count：
SCOPUS Cited Count： 15
ESI Highly Cited Papers on the List： 0 Unfold All
WanFang Cited Count：
Chinese Cited Count：
30 Days PV： 6
Affiliated Colleges：
