J
Jon Arrospide
Researcher at Technical University of Madrid
Publications - 19
Citations - 525
Jon Arrospide is an academic researcher from Technical University of Madrid. The author has contributed to research in topics: Object detection & Robustness (computer science). The author has an hindex of 11, co-authored 19 publications receiving 481 citations. Previous affiliations of Jon Arrospide include Altran.
Papers
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Journal ArticleDOI
Log-Gabor Filters for Image-Based Vehicle Verification
Jon Arrospide,Luis Salgado +1 more
TL;DR: The extensive experiments conducted in this paper confirm that the proposed log-Gabor descriptor significantly outperforms the standard Gabor filter for image-based vehicle verification.
Proceedings ArticleDOI
Robust multiple lane road modeling based on perspective analysis
TL;DR: A new monocular image processing strategy that achieves a robust multiple lane model is proposed, showing the robustness of the system, delivering accurate multiple lane road models in most situations.
Journal ArticleDOI
Video analysis-based vehicle detection and tracking using an MCMC sampling framework
TL;DR: A likelihood model that combines appearance analysis with information from motion parallax is introduced for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera to address the main shortcomings of traditional particle filtering approaches.
Journal ArticleDOI
Homography-based ground plane detection using a single on-board camera
TL;DR: A novel homography calculation method based on a linear estimation framework that is specially suited for challenging environments, in particular traffic scenarios, in which the information is scarce and the homography computed from the images is usually inaccurate or erroneous.
Journal ArticleDOI
Image-based on-road vehicle detection using cost-effective Histograms of Oriented Gradients
TL;DR: Less-demanding HOG descriptors are proposed and evaluated that significantly lighten the computation by exploiting the a priori known vehicle appearance, rendering HOG-based real-time vehicle detection affordable, while achieving detection rates of over 96%.