In this paper we extend a segmentation method aimed at separating the moving objects from the background in a generic video sequence by means of an Higher Order Statistics (HOS) significance test performed on a group of inter-frame differences. The test is followed by the motion detection phase, producing a preliminary binary segmentation map, that is refined by a final regularization stage. The HOS threshold and the temporal extent of the motion detection phase are adaptively changed on the basis of the estimated background activity and of the detected presence of slowly moving objects. The regularization phase, imposing a local connectivity constraint on the background-foreground map by basic morphological operators, plays an important role in eliminating misclassifications due to motion estimation ambiguities, of the original video sequence. The algorithm performance is illustrated by typical results obtained on MPEG4 sequences.

Neri, A., Colonnese, S., Russo, G., & Tabacco, C. (1997). Adaptive segmentation of moving object versus background for video coding. In Proceedings of SPIE - The International Society for Optical Engineering (pp.443-453) [10.1117/12.279565].

Adaptive segmentation of moving object versus background for video coding

NERI, Alessandro;
1997

Abstract

In this paper we extend a segmentation method aimed at separating the moving objects from the background in a generic video sequence by means of an Higher Order Statistics (HOS) significance test performed on a group of inter-frame differences. The test is followed by the motion detection phase, producing a preliminary binary segmentation map, that is refined by a final regularization stage. The HOS threshold and the temporal extent of the motion detection phase are adaptively changed on the basis of the estimated background activity and of the detected presence of slowly moving objects. The regularization phase, imposing a local connectivity constraint on the background-foreground map by basic morphological operators, plays an important role in eliminating misclassifications due to motion estimation ambiguities, of the original video sequence. The algorithm performance is illustrated by typical results obtained on MPEG4 sequences.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/305254
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