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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-L1 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-L1 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.
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Source :
2015 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO)
Year: 2015
Page: 84-89
Language: English
Cited Count:
WoS CC Cited Count: 10
SCOPUS Cited Count:
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 9
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