In this paper we address the problem of fast segmenting moving objects in video acquired by moving camera or more generally with a moving background. We present an approach based on a color segmentation followed by a region-merging on motion through Markov Random Fields (MRFs). The technique we propose is inspired to a work of Gelgon and Bouthemy [6], that has been modified to reduce computational cost in order to achieve a fast segmentation (about ten frame per second). To this aim a modified region matching algorithm (namely Partitioned Region Matching) and an innovative arc-based MRF optimization algorithmwith a suitable definition of the motion reliability are proposed. Results on both synthetic and real sequences are reported to confirm validity of our solution.
Object Segmentation in Videos from Moving Camera with MRFs on Color and Motion Features / Cucchiara, Rita; Prati, Andrea; Vezzani, Roberto. - STAMPA. - 1:(2003), pp. 405-410. (Intervento presentato al convegno 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition tenutosi a Madison, WI, usa nel 16-22 June).
Object Segmentation in Videos from Moving Camera with MRFs on Color and Motion Features
CUCCHIARA, Rita;PRATI, Andrea;VEZZANI, Roberto
2003
Abstract
In this paper we address the problem of fast segmenting moving objects in video acquired by moving camera or more generally with a moving background. We present an approach based on a color segmentation followed by a region-merging on motion through Markov Random Fields (MRFs). The technique we propose is inspired to a work of Gelgon and Bouthemy [6], that has been modified to reduce computational cost in order to achieve a fast segmentation (about ten frame per second). To this aim a modified region matching algorithm (namely Partitioned Region Matching) and an innovative arc-based MRF optimization algorithmwith a suitable definition of the motion reliability are proposed. Results on both synthetic and real sequences are reported to confirm validity of our solution.
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