Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Marcos Nieto"'
Publikováno v:
Vehicles, Vol 4, Iss 1, Pp 42-59 (2022)
Local dynamic map (LDM) is a key component in the future of autonomous and connected vehicles. An LDM serves as a local database with the necessary tools to have a common reference system for both static data (i.e., map information) and dynamic data
Externí odkaz:
https://doaj.org/article/bedca0cb7af64ba7b2dbb071ab96ef5a
Publikováno v:
SoftwareX, Vol 13, Iss , Pp 100653- (2021)
Data labeling has become a major problem in industries aiming to create and use ground truth labels from massive multi-sensor archives to feed into Artificial Intelligence (AI) applications. Annotation of multi-sensor set-ups with multiple cameras an
Externí odkaz:
https://doaj.org/article/04c6a30ded894891a45e2a6f3f67bd7a
Publikováno v:
Sensors, Vol 22, Iss 7, p 2554 (2022)
Tremendous advances in advanced driver assistance systems (ADAS) have been possible thanks to the emergence of deep neural networks (DNN) and Big Data (BD) technologies. Huge volumes of data can be managed and consumed as training material to create
Externí odkaz:
https://doaj.org/article/7959c8e7f44c40c98f70bc564e4a5c6d
Publikováno v:
Applied Sciences, Vol 11, Iss 17, p 7782 (2021)
Modern Artificial Intelligence (AI) methods can produce a large quantity of accurate and richly described data, in domains such as surveillance or automation. As a result, the need to organize data at a large scale in a semantic structure has arisen
Externí odkaz:
https://doaj.org/article/9474f59565ff46558f166ce5c42de8bf
Publikováno v:
Applied Sciences, Vol 10, Iss 12, p 4301 (2020)
An innovative solution named Annotation as a Service (AaaS) has been specifically designed to integrate heterogeneous video annotation workflows into containers and take advantage of a cloud native highly scalable and reliable design based on Kuberne
Externí odkaz:
https://doaj.org/article/964ffeb0632a4d1ab54d9a7b4f2bc378
Publikováno v:
IET Computer Vision, Vol 10, Iss 4, Pp 235-236 (2016)
Externí odkaz:
https://doaj.org/article/7129b306c74f43958844b27ffc9494a0
Autor:
David Montero, Nerea Aranjuelo, Peter Leskovsky, Estíbaliz Loyo, Marcos Nieto, Naiara Aginako
Publikováno v:
Multimedia Tools and Applications.
The current sanitary emergency situation caused by COVID-19 has increased the interest in controlling the flow of people in indoor infrastructures, to ensure compliance with the established security measures. Top view camera-based solutions have prov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a182d710c37af4818ede98babd340927
https://doi.org/10.1007/s11042-023-14416-y
https://doi.org/10.1007/s11042-023-14416-y
Autor:
Julian Florez Esnal, Andoni Cortes Vidal, Oihana Otaegui Madurga, Marcos Nieto Doncel, Manuel Graña Romay, Javier Barandiaran Martirena
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 23:3115-3125
Lane markings are a key element for Autonomous Driving. The generation of high definition maps and ground-truth data require extensive manual labor. In this paper, we present an efficient and robust method for the offline annotation of lane markings,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6293f18d276ab50cbceeb25f1cfbc970
https://doi.org/10.1109/tits.2020.3031921
https://doi.org/10.1109/tits.2020.3031921
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 23:190-199
A major challenges of deep learning (DL) is the necessity to collect huge amounts of training data. Often, the lack of a sufficiently large dataset discourages the use of DL in certain applications. Typically, acquiring the required amounts of data c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7c7f1d112cb657a1f8a31d8aded53487
https://doi.org/10.1109/tits.2020.3009186
https://doi.org/10.1109/tits.2020.3009186
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031250743
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::852b1d7dd69ad57f3025fd6f559183ef
https://doi.org/10.1007/978-3-031-25075-0_38
https://doi.org/10.1007/978-3-031-25075-0_38
