Coupled multi-object tracking and labeling for vehicle trajectory estimation and matching

Doulamis, ND

dc.contributor.author	Doulamis, ND	en
dc.date.accessioned	2014-03-01T01:33:05Z
dc.date.available	2014-03-01T01:33:05Z
dc.date.issued	2010	en
dc.identifier.issn	1380-7501	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20311
dc.subject	Motion-based tracking	en
dc.subject	Neural networks	en
dc.subject	Non linear object labelling	en
dc.subject.classification	Computer Science, Information Systems	en
dc.subject.classification	Computer Science, Software Engineering	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Detection and tracking algorithms	en
dc.subject.other	Efficient detection	en
dc.subject.other	Engineering problems	en
dc.subject.other	Environmental change	en
dc.subject.other	Illumination variation	en
dc.subject.other	Motion-based tracking	en
dc.subject.other	Moving vehicles	en
dc.subject.other	Multi-object tracking	en
dc.subject.other	Non-linear	en
dc.subject.other	Non-Linearity	en
dc.subject.other	Object Tracking	en
dc.subject.other	Recursive identification	en
dc.subject.other	Research issues	en
dc.subject.other	Safe designs	en
dc.subject.other	Trajectory matching	en
dc.subject.other	Vehicle trajectories	en
dc.subject.other	Visual properties	en
dc.subject.other	Neural networks	en
dc.subject.other	Roads and streets	en
dc.subject.other	Surveys	en
dc.subject.other	Trajectories	en
dc.subject.other	Tracking (position)	en
dc.title	Coupled multi-object tracking and labeling for vehicle trajectory estimation and matching	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s11042-009-0370-0	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s11042-009-0370-0	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	Efficient detection and tracking of moving objects in real life conditions is a very challenging research issue, mainly due to occlusions, illumination variations, appearance (disappearance) of new (existing) objects and overlapping issues. In this paper, we address these difficulties by incorporating non-linear and recursive identification mechanisms in motion-based detection and tracking algorithms. Non-linearity allows correct identification of object of complex visual properties while the adaptability makes the proposed scheme able to update its behaviour to the dynamic environmental changes. In addition, in this paper, we introduce the concept of polar spectrum which is a measure for determining the deviation of a vehicle trajectory from an ideal trace. The proposed methods (object tracking and trajectory matching) are applied in survey engineering problems dealing with safe design road turns. In particular, the automatically detected trajectory of a moving vehicle is compared with the ideal trace, through the polar spectrum measure, to determine the safety of a road turn. This trace is also compared with the one manually derived using photogrammetric algorithms and a small error is obtained verifying the efficiency of the method. © 2009 Springer Science+Business Media, LLC.	en
heal.publisher	SPRINGER	en
heal.journalName	Multimedia Tools and Applications	en
dc.identifier.doi	10.1007/s11042-009-0370-0	en
dc.identifier.isi	ISI:000279198900009	en
dc.identifier.volume	50	en
dc.identifier.issue	1	en
dc.identifier.spage	173	en
dc.identifier.epage	198	en