Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks

Autor: Vicent Ortiz Castello, Francisco Javier Perez-Benito, Omar Del Tejo Catala, Ismael Salvador Igual, Rafael Llobet, Juan-Carlos Perez-Cortes

Publikováno v: IEEE Access, Vol 10, Pp 120074-120085 (2022)

The a priori probability of a dataset is usually used as a baseline for comparing a particular algorithm’s accuracy in a given binary classification task. ZeroR is the simplest algorithm for this, predicting the majority class for all examples. How

Externí odkaz: https://doaj.org/article/56a3bbcdbb8040c2a32649c983008814

Probabilistic Pose Estimation From Multiple Hypotheses

Autor: Omar Del-Tejo-Catala, Jose-Luis Guardiola, Javier Perez, David Millan Escriva, Alberto J. Perez, Juan-Carlos Perez-Cortes

Publikováno v: IEEE Access, Vol 11, Pp 64507-64517 (2023)

Pose estimation assesses the 6D pose of one or many objects in a scene. Considerable attention has been dedicated to the advancement of pose estimation algorithms capable of identifying the orientation of multiple objects within a scene in cases wher

Externí odkaz: https://doaj.org/article/a77be3425efc4a478b93a6235a1fd9ea

Real-time on-board pedestrian detection using generic single-stage algorithms and on-road databases

Autor: V. Ortiz, Juan-Carlos Perez-Cortes, Omar del Tejo Catala, Ismael Salvador Igual

Publikováno v: International Journal of Advanced Robotic Systems, Vol 17 (2020)
International Journal of Advanced Robotic Systems
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
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[EN] Pedestrian detection is a particular case of object detection that helps to reduce accidents in advanced driver-assistance systems and autonomous vehicles. It is not an easy task because of the variability of the objects and the time constraints

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5fa17b849dff17d4301d46016ca6b65b
https://doaj.org/article/685f9ae0bdf146ebb4fff7025ecb7905

High-Profile VRU Detection on Resource-Constrained Hardware Using YOLOv3/v4 on BDD100K

Autor: Vicent Ortiz Castelló, Ismael Salvador Igual, Omar del Tejo Catalá, Juan-Carlos Perez-Cortes

Publikováno v: Journal of Imaging, Vol 6, Iss 12, p 142 (2020)

Vulnerable Road User (VRU) detection is a major application of object detection with the aim of helping reduce accidents in advanced driver-assistance systems and enabling the development of autonomous vehicles. Due to intrinsic complexity present in

Externí odkaz: https://doaj.org/article/f1395799e3784a92b759de464ea3d89b