High Level Sensor Data Fusion Approaches For Object Recognition In Road Environment

Floudas, N; Polychronopoulos, A; Aycard, O; Burlet, J; Ahrholdt, M

dc.contributor.author	Floudas, N	en
dc.contributor.author	Polychronopoulos, A	en
dc.contributor.author	Aycard, O	en
dc.contributor.author	Burlet, J	en
dc.contributor.author	Ahrholdt, M	en
dc.date.accessioned	2014-03-01T02:50:58Z
dc.date.available	2014-03-01T02:50:58Z
dc.date.issued	2007	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35255
dc.subject	Data Association	en
dc.subject	multi-sensor data fusion	en
dc.subject	Object Recognition	en
dc.subject	Sensor Data Fusion	en
dc.subject	Sensor Network	en
dc.subject	Data Fusion	en
dc.subject	Field of View	en
dc.subject	Point To Point	en
dc.title	High Level Sensor Data Fusion Approaches For Object Recognition In Road Environment	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IVS.2007.4290104	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IVS.2007.4290104	en
heal.publicationDate	2007	en
heal.abstract	Application of high level fusion approaches demonstrate a sequence of significant advantages in multi sensor data fusion and automotive safety fusion systems are no exception to this. High level fusion can be applied to automotive sensor networks with complementary or/and redundant field of views. The advantage of this approach is that it ensures system modularity and allows benchmarking, as it	en
heal.journalName	Intelligent Vehicle, IEEE Symposium	en
dc.identifier.doi	10.1109/IVS.2007.4290104	en