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Displaying 1-4 out of 4 total
Random Forests of Local Experts for Pedestrian Detection
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Javier Marin,David Vazquez,Antonio M. Lopez,Jaume Amores,Bastian Leibe
Issue Date:December 2013
pp. 2592-2599
Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with ...
Learning appearance in virtual scenarios for pedestrian detection
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Javier Marin, David Vazquez, David Geronimo, Antonio M. Lopez
Issue Date:June 2010
pp. 137-144
Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled samples. This paper addresses the following question: can a...
Virtual and Real World Adaptation for Pedestrian Detection
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By David Vazquez,Antonio M. Lopez,Javier Marin,Daniel Ponsa,David Geronimo
Issue Date:April 2014
pp. 797-809
Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, i.e., trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to ...
Virtual worlds and active learning for human detection
Found in: Proceedings of the 13th international conference on multimodal interfaces (ICMI '11)
By Antonio M. Lopez, Daniel Ponsa, David Vazquez, Javier Marin
Issue Date:November 2011
pp. 393-400
Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive res...