An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency-based video segmentation with graph cuts and sequentially updated priors
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Video segmentation using iterated graph cuts based on spatio-temporal volumes
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
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The traditional graph-cut for video moving objects detection is a global optimization algorithm, the result may be over-smoothing. The lack of local information in graph-cut limits the ability to precisely localize object boundaries. In this paper, moving objects detection algorithm is improved by introducting geodesic active contour. By the Kalman prediction of the number of objectives pixels and objectives-background pixel-pairs, and adaptive updating of the nodes flux with geodesic active contour, the proposed algorithm is successfully applied to video moving objects detection. Though adaptive updating of the nodes flux with geodesic active contour, the proposed algorithm will have a better edge capture ability of moving objects. Experimental results show that the proposed algorithm is more effective than graph-cut for video moving objects detection in complex backgrounds.