Rule-driven object tracking in clutter and partial occlusion with model-based snakes

Tsechpenakis, G; Rapantzikos, K; Tsapatsoulis, N; Kollias, S

dc.contributor.author	Tsechpenakis, G	en
dc.contributor.author	Rapantzikos, K	en
dc.contributor.author	Tsapatsoulis, N	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T01:21:18Z
dc.date.available	2014-03-01T01:21:18Z
dc.date.issued	2004	en
dc.identifier.issn	1110-8657	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16197
dc.subject	Model-based snakes	en
dc.subject	Object partial occlusion	en
dc.subject	Rule-driven tracking	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Algorithms	en
dc.subject.other	Approximation theory	en
dc.subject.other	Computational complexity	en
dc.subject.other	Estimation	en
dc.subject.other	Image quality	en
dc.subject.other	Mathematical models	en
dc.subject.other	Problem solving	en
dc.subject.other	Model-based snakes	en
dc.subject.other	Object partial occlusion	en
dc.subject.other	Rule-driven tracking	en
dc.subject.other	Video sequences	en
dc.subject.other	Computer vision	en
dc.title	Rule-driven object tracking in clutter and partial occlusion with model-based snakes	en
heal.type	journalArticle	en
heal.identifier.primary	10.1155/S1110865704401103	en
heal.identifier.secondary	http://dx.doi.org/10.1155/S1110865704401103	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	In the last few years it has been made clear to the research community that further improvements in classic approaches for solving low-level computer vision and image/video understanding tasks are difficult to obtain. New approaches started evolving, employing knowledge-based processing, though transforming a priori knowledge to low-level models and rules are far from being straightforward. In this paper, we examine one of the most popular active contour models, snakes, and propose a snake model, modifying terms and introducing a model-based one that eliminates basic problems through the usage of prior shape knowledge in the model. A probabilistic rule-driven utilization of the proposed model follows, being able to handle (or cope with) objects of different shapes, contour complexities and motions; different environments, indoor and outdoor; cluttered sequences; and cases where background is complex (not smooth) and when moving objects get partially occluded. The proposed method has been tested in a variety of sequences and the experimental results verify its efficiency.,	en
heal.publisher	HINDAWI PUBLISHING CORPORATION	en
heal.journalName	Eurasip Journal on Applied Signal Processing	en
dc.identifier.doi	10.1155/S1110865704401103	en
dc.identifier.isi	ISI:000223382900006	en
dc.identifier.volume	2004	en
dc.identifier.issue	6	en
dc.identifier.spage	841	en
dc.identifier.epage	860	en