Miguel Angel Sotelo
University of Alcalá
265 Papers
1.2K Citations
Miguel Angel Sotelo is an academic researcher from University of Alcalá. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 35, co-authored 235 publications. Previous affiliations of Miguel Angel Sotelo include University of Alabama.
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Papers
Creating navigation map in semi-open scenarios for intelligent vehicle localization using multi-sensor fusion
Li Yicheng,Li Yicheng,Yingfeng Cai,Reza Malekian,Hai Wang,Miguel Angel Sotelo,Zhixiong Li,Zhixiong Li +7 more
TL;DR: The proposed RSF map can be applied to semi-open scenarios in practice to provide a reliable basic for IV localization and demonstrate that the mean error of the nodes between the created and actual maps was 2.7 cm.
18
Lightweight Occupancy Estimation on Freeways Using Extended Floating Car Data
TL;DR: A lightweight method to perform “on-line” occupancy estimation that is able to reproduce the occupancy values generated by the actual loop detectors, achieving promising results, with estimation errors down to 6.52%, even before multivehicle systems are considered.
18
Automation of an Industrial Fork Lift Truck, Guided by Artificial Vision in Open Environments
TL;DR: A platform, on the base of a commercial industrial truck, provided with sufficient autonomy to carry out tasks within an industrial environment, using a system of artificial vision which enables it to move on asphalted surfaces both in open environments (roads) and closed ones.
Real-time robust face tracking for driver monitoring
J. Nuevo,Luis M. Bergasa,Miguel Angel Sotelo,Manuel Ocaña +3 more
- 09 Oct 2006
TL;DR: An active appearance model and a fitting algorithm to track a driver's face, as a component for a driver alertness monitoring system, which is very efficient and able to run in real-time.
3D Candidate Selection Method for Pedestrian Detection on Non-Planar Roads
D. Fernandez,Ignacio Parra,Miguel Angel Sotelo,P. Revenga,S. Alvarez,M. Gavilan +5 more
- 13 Jun 2007
TL;DR: A stereo-vision-based candidate selection method for pedestrian detection from a moving vehicle using Subtractive Clustering algorithm in a 3D space with an adaptive radius that can be configurable for different types of obstacles.