Fuzzy Logic-Based Scenario Recognition from Video Sequences
Elba, E.
Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM
dor_id: 45663
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650.#.4.x: Ingenierías
336.#.#.b: article
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351.#.#.b: Journal of Applied Research and Technology
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856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/276/273
100.1.#.a: Elba, E.
524.#.#.a: Elba, E. (2013). Fuzzy Logic-Based Scenario Recognition from Video Sequences. Journal of Applied Research and Technology; Vol. 11 Núm. 5. Recuperado de https://repositorio.unam.mx/contenidos/45663
245.1.0.a: Fuzzy Logic-Based Scenario Recognition from Video Sequences
502.#.#.c: Universidad Nacional Autónoma de México
561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM
264.#.0.c: 2013
264.#.1.c: 2013-10-01
653.#.#.a: Scenario recognition; high level processing; control chart; fuzzy logic
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
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520.3.#.a: In recent years, video surveillance and monitoring have gained importance because of security and safety concerns.Banks, borders, airports, stores, and parking areas are the important application areas. There are two main parts inscenario recognition Low level processing, including moving object detection and object tracking, and featureextraction. We have developed new features through this work which are RUD (relative upper density), RMD (relativemiddle density) and RLD (relative lower density), and we have used other features such as aspect ratio, width, height,and color of the object. High level processing, including event start-end point detection, activity detection for eachframe and scenario recognition for sequence of images. This part is the focus of our research, and different patternrecognition and classification methods are implemented and experimental results are analyzed. We looked intoseveral methods of classification which are decision tree, frequency domain classification, neural network-basedclassification, Bayes classifier, and pattern recognition methods, which are control charts, and hidden Markov models.The control chart approach, which is a decision methodology, gives more promising results than other methodologies.Overlapping between events is one of the problems, hence we applied fuzzy logic technique to solve this problem.After using this method the total accuracy increased from 95.6 to 97.2.
773.1.#.t: Journal of Applied Research and Technology; Vol. 11 Núm. 5
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/10.1016/S1665-6423(13)71578-5
harvesting_date: 2023-11-08 13:10:00.0
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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
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Elba, E.
Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM
Elba, E. (2013). Fuzzy Logic-Based Scenario Recognition from Video Sequences. Journal of Applied Research and Technology; Vol. 11 Núm. 5. Recuperado de https://repositorio.unam.mx/contenidos/45663