Object Detection and Classification for ADAS and Autonomous Driving

Please use this identifier to cite or link to this item: https://hdl.handle.net/1889/3420

Full metadata record

DC Field	Value	Language
dc.contributor.advisor	Bertozzi, Massimo	-
dc.contributor.author	Castangia, Luca	-
dc.date.accessioned	2017-07-11T11:51:43Z	-
dc.date.available	2017-07-11T11:51:43Z	-
dc.date.issued	2017-03-24	-
dc.identifier.uri	http://hdl.handle.net/1889/3420	-
dc.description.abstract	A reliable and robust perception system of the real world is a necessary for an autonomous vehicle and the Advanced Driver Assistance Systems. Obstacles detection and classification are the main pillar for the correct understanding of the dynamic world. The system can provide a reconstruction of the dynamic world surrounding the vehicle, proving to be able to help the driver in the assessment of critical situations. In particular, the developed algorithm provides a stable, robust and reliable detection, classification and tracking of the multiple targets coming from different cameras.	it
dc.language.iso	Inglese	it
dc.publisher	Università di Parma. Dipartimento di Ingegneria dell'Informazione	it
dc.relation.ispartofseries	Dottorato di ricerca in Tecnologie dell'informazione	it
dc.rights	© Luca Castangia, 2017	it
dc.subject	object detection, object tracking, classification and tracking	it
dc.subject	visione artificiale	it
dc.title	Object Detection and Classification for ADAS and Autonomous Driving	it
dc.title.alternative	Rilevamento di oggetti e classificazione per la guida di veicoli autonomi	it
dc.type	Doctoral thesis	it
dc.subject.miur	ING-INF/05	it
Appears in Collections:	Tecnologie dell'informazione. Tesi di dottorato

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