dc.contributorhttps://orcid.org/0000-0002-2746-8733
dc.contributorhttps://orcid.org/0000-0002-6084-2512
dc.contributorhttps://scholar.google.com/citations?user=vncSAb0AAAA
dc.contributorhttps://scholar.google.com/citations?user=tJG988kAAAAJ
dc.contributorhttp://scienti.colciencias.gov.co:8081/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001630084
dc.contributorhttp://scienti.colciencias.gov.co:8081/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001516111
dc.creatorPérez Gordillo, Fabian Eduardo
dc.creatorCamacho Poveda, Edgar Camilo
dc.date.accessioned2020-04-20T17:15:44Z
dc.date.accessioned2022-09-28T13:43:38Z
dc.date.available2020-04-20T17:15:44Z
dc.date.available2022-09-28T13:43:38Z
dc.date.created2020-04-20T17:15:44Z
dc.date.issued2019-08
dc.identifierhttp://hdl.handle.net/11634/22650
dc.identifierhttps://doi.org/10.15332/dt.inv.2020.01521
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3644875
dc.description.abstractThe Robotics Study and Development Group - GED of the Faculty of Electronic Engineering is developing a macro-project in Social Robotics using the Pepper robotic platform as a service robot, in daily environments for men and women, so that this have basic skills to carry out common tasks that improve human well-being. Framed within this objective, this proposal aims to develop a group of useful software tools for the execution of tasks on the Pepper robotic platform that include: tools for object recognition, tools for the construction of the DataSet, the definition of categories and classes for training and that also integrate additional functionality to provide object-specific information.
dc.relationL. B. a. L. I. Nizar Massouh, «RoboCup@Home-Objects: benchmarking object recognition for home robots,» RoboCup 2019, 2019.
dc.relationSoftBank Robotics, «Pepper,» [En línea]. Available: https://www.softbankrobotics.com/emea/en/pepper. [Último acceso: 07 2019
dc.relationC. M. U. R. T. C. U. o. P. U. o. S. C. S. U. U. o. C.-B. U. o. W. U. o. S. C. S. U. U. o. C.-B. U. o. W. G. I. of Technology, «A Roadmap for U.S. Robotics: From Internet to Robotics,» Georgia Institute of Technology, 2013. [En línea]. Available: https://books.google.com.co/books?id=KPhQngEACAAJ.
dc.relationS. c/o euRobotics AISBL, Robotics 2020 Multi-Annual Road-map For Robotics in Europe, 2015.
dc.relationM. A. Y. K. I. N. a. E. O. H. Kitano, «Robocup: The robot world cup initiative,» of the First International Conference on Autonomous Agents, ser. AGENTS, pp. 340 - 347, 1997
dc.relationRoboCup@Home, «Team Description Papers,» [En línea]. Available: https://github.com/RoboCupAtHome/AtHomeCommunityWiki/wiki/Team-DescriptionPapers. [Último acceso: 07 2019]
dc.relationE. R. F. P. S. R. S. T. M. U. M. U. M. a. C.-I. M. Carlos A. Quintero, «2019 SinfonIA Pepper Team Description Paper,» 2019.
dc.relationRoboCup, «RoboCup@Home,» [En línea]. Available: http://www.robocupathome.org/media. [Último acceso: 07 2019]
dc.relationD. H. J. R.-d.-S. K. S. T. v. d. Z. Luca Iocchi, «RoboCup@Home: Analysis and results of evolving competitions for domestic and service robots,» Artificial Intelligence, vol. 229, pp. 258 - 281, 2015.
dc.relationT. v. d. Z. L. I. a. S. S. Thomas Wisspeintner, «RoboCup@Home: Scientific Competition and Benchmarking for Domestic Service Robots,» 2009.
dc.relationCarnegie Mellon University, «Carmen, Robot Navigation Toolkit,» [En línea]. Available: (http://carmen.sourceforge.net/
dc.relation«The Player/Stage Project,» [En línea]. Available: (http://playerstage.sourceforge.net/.
dc.relationThe Mobile Robot Programming Toolkit,» [En línea]. Available: http://babel.isa.uma.es/mrpt/index.php/Main Page.
dc.relationOpenCV, «The Open Computer Vision Library,» [En línea]. Available: (http://sourceforge.net/projects/opencv/
dc.relation«ReadyBot,» [En línea]. Available: http://www.readybot.com/.
dc.relation«PR2,» [En línea]. Available: http://www.willowgarage.com.
dc.relation«Wakamaru,» [En línea]. Available: http://www.mhi.co.jp/kobe/wakamaru/english.
dc.relation«PaPeRo,» [En línea]. Available: http://www.nec.co.jp/robot/english/robotcenter e.htm
dc.relationToyota, «Partner Robot FAMILY,» [En línea]. Available: https://www.toyota-global.com/innovation/partner_robot/robot/.
dc.relationSoftBank Robotics, «Pepper Documentation,» [En línea]. Available: http://doc.aldebaran.com/2-4/home_pepper.html.
dc.relationF. S. N. Z. R. F. a. M. C. Dong Seon Cheng, «Semantically-driven automatic creation of training sets for object recognition,» Computer Vision and Image Understanding, vol. 131, pp. 56-71, 2015
dc.relationA. S. I. H. G. Krizhevsky, «Imagenet classification with deep convolutional neural networks,» Proc NIPS, 2012.
dc.relationC. L. W. J. Y. S. P. R. S. A. D. E. D. V. V. R. A. Szegedy, «Going deeper with convolutions. CoRR,» 2014.
dc.relation«Yolo,» [En línea]. Available: https://pjreddie.com/darknet/yolo/. [Último acceso: 07 2019].
dc.relationD. A. D. E. C. S. a. S. R. W. Liu, «SSD: Single shot multibox detector,» ECCV, 2016.
dc.relationP. G. R. G. K. H. P. D. a. F. A. R. (. Tsung-Yi Lin, «Focal Loss for Dense Object Detection,» ICCV, nº 2980 - 2988.
dc.relationA. F. Joseph Redmon, «YOLOv3: An Incremental Improvement,» 2018.
dc.relationL. D. ] L. Von Ahn, «Labeling images with a computer game,» Proceedings of the SIGCHI Conference on Human factors in Computing Systems, ACM, pp. 319 - 326, 2014.
dc.relationG. W. C.-W. N. Y.-G. J. S. Zhu, «On the sampling of web images for learning visual concept classifiers,» Proceedings of the ACM International Conference on Image and Video Retrieval, ACM, pp. 50 - 57, 2010.
dc.relationW. D. R. S. L.-J. L. K. L. L. F.-F. J. Deng, «ImageNet: a large-scale hierarchical image database,» IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248 - 255, 2009.
dc.relationM. R. M. S. J. S. S. V. J. Y. T. Dean, «Fast, accurate detection of 100,000 object classes on a single machine,» IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1814 - 1821, 2013.
dc.relationROS.org, «ROS,» [En línea]. Available: http://wiki.ros.org/. [Último acceso: 07 2019].
dc.relationROS.org, «vision_opencv,» [En línea]. Available: http://wiki.ros.org/vision_opencv.
dc.relationF. B. T. T. J. Y. N. H. a. B. C. Nizar Massouh, «Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work,» 2017.
dc.relationA. K. T. M. A. T. C. Vondrick, «HOGgles: visualizing object detection features,» IEEE International Conference on Computer Vision (ICCV), 2013.
dc.relationC. F. (Ed.), «WordNet: An Electronic Lexical Database,» MIT Press, 1998.
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia
dc.titleHerramienta para reconocimiento de objetos para la plataforma robótica pepper
dc.typeFormación de Recurso Humano para la Ctel: Proyecto ejecutado con investigadores en empresas, industrias y Estado


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