dor_id: 45597

506.#.#.a: Público

590.#.#.d: Los artículos enviados a la revista "Journal of Applied Research and Technology", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation

561.#.#.u: https://www.icat.unam.mx/

650.#.4.x: Ingenierías

336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

590.#.#.c: Open Journal Systems (OJS)

270.#.#.d: MX

270.1.#.d: México

590.#.#.b: Concentrador

883.#.#.u: https://revistas.unam.mx/catalogo/

883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/355/352

100.1.#.a: Hernandez Heredia, Y.; González Linares, J.M.a; Guil, N.; Ortiz, J.; Hernandez, R.; Cózar, J.R.

524.#.#.a: Hernandez Heredia, Y., et al. (2012). Object Detection with Vocabularies of Space-time Descriptors. Journal of Applied Research and Technology; Vol. 10 Núm. 6. Recuperado de https://repositorio.unam.mx/contenidos/45597

245.1.0.a: Object Detection with Vocabularies of Space-time Descriptors

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2012

264.#.1.c: 2012-12-01

653.#.#.a: object detection; video segmentation; vocabulary; binary classifier

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/355

001.#.#.#: 074.oai:ojs2.localhost:article/355

041.#.7.h: eng

520.3.#.a: This paper presents a novel framework for objects detection in security and broadcast videos. Our method assumes thatobject classes are unknown in advance and exploit the temporal-space properties of the videos for the creation of avocabulary that describes these classes. Local space-time features have recently became a popular video representationfor action recognition and object detection. Several methods for feature localization and description have been proposedin the literature and promising recognition results were demonstrated for a number of action classes.In this work we propose the use of different kinds of descriptors for the creation of vocabularies for different detectionobject task. For a better description of the videos we carry out a background model, tryring to clean up and follow theareas where there are objects. The points of interest in the videos to characterize the objects are calculated with atemporary variant of the famous Harris corner detector. With the descriptors obtained from the points of interest, avocabulary is realized usingthe kinds of videos we want to train. Then we obtained the frequency histogramsbetween the videos for training and the vocabulary so, with a binary classifier obtain the trained classes and followingthe same procedure without the vocabulary realized the detection and monitoring of the objects.The new method presented is also compared with a state of the art method, obtaining better results in both accuracyand false object rejection.

773.1.#.t: Journal of Applied Research and Technology; Vol. 10 Núm. 6

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.22201/icat.16656423.2012.10.6.355

harvesting_date: 2023-11-08 13:10:00.0

856.#.0.q: application/pdf

last_modified: 2024-03-19 14:00:00

license_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es

license_type: by-nc-sa

_deleted_conflicts: 2-24a08d8c7c5c91882d510b049e0b5e60

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