An algorithm for object-oriented motion estimation is presented. The algorithm initially determines a macro-block partition on the basis of the computed current frame difference, using the hidden information that a foreground moving object produces high absolute frame difference values in the neighborhood of the object boundaries. An inter-frame coding algorithm, adopting a modified version of classical block matching is then applied on separate slices of each macro-block. Resulting data are used to obtain a preliminary object segmentation. The algorithm further splits each macro-block if characterized by the presence of more than one motion vector into sub-areas. The approach allows us to obtain a global object segmentation-with the possibility of tracking-and lower prediction errors with respect to classical block matching, without increasing the computational complexity.
Object oriented motion estimation by sliced-block matching algorithm / Guaragnella, Cataldo; DI SCIASCIO, Eugenio. - 15:3(2000), pp. 857-860. (Intervento presentato al convegno IEEE International Conference on Pattern Recognition, ICPR 2000 tenutosi a Barcekona, Spain nel September 03-07, 2000) [10.1109/ICPR.2000.903678].
Object oriented motion estimation by sliced-block matching algorithm
GUARAGNELLA, Cataldo;DI SCIASCIO, Eugenio
2000-01-01
Abstract
An algorithm for object-oriented motion estimation is presented. The algorithm initially determines a macro-block partition on the basis of the computed current frame difference, using the hidden information that a foreground moving object produces high absolute frame difference values in the neighborhood of the object boundaries. An inter-frame coding algorithm, adopting a modified version of classical block matching is then applied on separate slices of each macro-block. Resulting data are used to obtain a preliminary object segmentation. The algorithm further splits each macro-block if characterized by the presence of more than one motion vector into sub-areas. The approach allows us to obtain a global object segmentation-with the possibility of tracking-and lower prediction errors with respect to classical block matching, without increasing the computational complexity.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.