Object flow: Learning object displacement

Lalos, C; Grabner, H; Van Gool, L; Varvarigou, T

dc.contributor.author	Lalos, C	en
dc.contributor.author	Grabner, H	en
dc.contributor.author	Van Gool, L	en
dc.contributor.author	Varvarigou, T	en
dc.date.accessioned	2014-03-01T02:53:23Z
dc.date.available	2014-03-01T02:53:23Z
dc.date.issued	2011	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36286
dc.subject.other	Action recognition	en
dc.subject.other	Detection and tracking	en
dc.subject.other	Dynamic behaviours	en
dc.subject.other	Learning objects	en
dc.subject.other	Motion fields	en
dc.subject.other	Motion representation	en
dc.subject.other	Moving objects	en
dc.subject.other	Object displacement	en
dc.subject.other	Object flow	en
dc.subject.other	Object Tracking	en
dc.subject.other	Scene description	en
dc.subject.other	Visual applications	en
dc.subject.other	Tracking (position)	en
dc.subject.other	Computer vision	en
dc.title	Object flow: Learning object displacement	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-22822-3_14	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-22822-3_14	en
heal.publicationDate	2011	en
heal.abstract	Modelling the dynamic behaviour of moving objects is one of the basic tasks in computer vision. In this paper, we introduce the Object Flow, for estimating both the displacement and the direction of an object-of-interest. Compared to the detection and tracking techniques, our approach obtains the object displacement directly similar to optical flow, while ignoring other irrelevant movements in the scene. Hence, Object Flow has the ability to continuously focus on a specific object and calculate its motion field. The resulting motion representation is useful for a variety of visual applications (e.g., scene description, object tracking, action recognition) and it cannot be directly obtained using the existing methods. © 2011 Springer-Verlag Berlin Heidelberg.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-22822-3_14	en
dc.identifier.volume	6468 LNCS	en
dc.identifier.issue	PART1	en
dc.identifier.spage	133	en
dc.identifier.epage	142	en