Autor/s de la URV: | Serratosa Casanelles, Francesc d'Assís |
Autor segons l'article: | Serratosa, Francesc; Alquezar, Rene; Amezquita, Nicolas |
Adreça de correu electrònic de l'autor: | francesc.serratosa@urv.cat |
Identificador de l'autor: | 0000-0001-6112-5913 |
Any de publicació de la revista: | 2012 |
Tipus de publicació: | Journal Publications |
Referència de l'ítem segons les normes APA: | Serratosa, Francesc; Alquezar, Rene; Amezquita, Nicolas (2012). A probabilistic integrated object recognition and tracking framework. Expert Systems With Applications, 39(8), 7302-7318. DOI: 10.1016/j.eswa.2012.01.088 |
Referència a l'article segons font original: | Expert Systems With Applications. 39 (8): 7302-7318 |
Resum: | This paper describes a probabilistic integrated object recognition and tracking framework called PIORT, together with two specific methods derived from it, which are evaluated experimentally in several test video sequences. The first step in the proposed framework is a static recognition module that provides class probabilities for each pixel of the image from a set of local features. These probabilities are updated dynamically and supplied to a tracking decision module capable of handling full and partial occlusions. The two specific methods presented use RGB color features and differ in the classifier implemented: one is a Bayesian method based on maximum likelihood and the other one is based on a neural network. The experimental results obtained have shown that, on one hand, the neural net based approach performs similarly and sometimes better than the Bayesian approach when they are integrated within the tracking framework. And on the other hand, our PIORT methods have achieved better results when compared to other published tracking methods in video sequences taken with a moving camera and including full and partial occlusions of the tracked object. © 2011 Elsevier Ltd. All rights reserved. |
DOI de l'article: | 10.1016/j.eswa.2012.01.088 |
Enllaç font original: | https://www.sciencedirect.com/science/article/abs/pii/S0957417412001017 |
Versió de l'article dipositat: | info:eu-repo/semantics/acceptedVersion |
Accès a la llicència d'ús: | https://creativecommons.org/licenses/by/3.0/es/ |
Departament: | Enginyeria Informàtica i Matemàtiques |
URL Document de llicència: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
Àrees temàtiques: | Química Operations research & management science Medicina iii Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Interdisciplinar Geociências General engineering Farmacia Engineering, electrical & electronic Engineering (miscellaneous) Engineering (all) Engenharias iv Engenharias iii Engenharias ii Engenharias i Enfermagem Educação Economia Direito Computer science, artificial intelligence Computer science applications Ciências sociais aplicadas i Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência da computação Biotecnología Biodiversidade Astronomia / física Artificial intelligence Arquitetura, urbanismo e design Administração, ciências contábeis e turismo Administração pública e de empresas, ciências contábeis e turismo |
Paraules clau: | Visual tracking Video sequences Video recording System Probabilistic methods Performance evaluation People Occlusion Object tracking Object recognition Multiple Integration Image segmentation Features Feature extraction Dynamic environments Bayesian networks Appearance models |
Entitat: | Universitat Rovira i Virgili |
Data d'alta del registre: | 2024-10-12 |
Descripció: | This paper describes a probabilistic integrated object recognition and tracking framework called PIORT, together with two specific methods derived from it, which are evaluated experimentally in several test video sequences. The first step in the proposed framework is a static recognition module that provides class probabilities for each pixel of the image from a set of local features. These probabilities are updated dynamically and supplied to a tracking decision module capable of handling full and partial occlusions. The two specific methods presented use RGB color features and differ in the classifier implemented: one is a Bayesian method based on maximum likelihood and the other one is based on a neural network. The experimental results obtained have shown that, on one hand, the neural net based approach performs similarly and sometimes better than the Bayesian approach when they are integrated within the tracking framework. And on the other hand, our PIORT methods have achieved better results when compared to other published tracking methods in video sequences taken with a moving camera and including full and partial occlusions of the tracked object. © 2011 Elsevier Ltd. All rights reserved. |
Tipus: | Journal Publications |
Coautor: | Universitat Rovira i Virgili |
Títol: | A probabilistic integrated object recognition and tracking framework |
Matèria: | Artificial Intelligence,Computer Science Applications,Computer Science, Artificial Intelligence,Engineering (Miscellaneous),Engineering, Electrical & Electronic,Operations Research & Management Science Visual tracking Video sequences Video recording System Probabilistic methods Performance evaluation People Occlusion Object tracking Object recognition Multiple Integration Image segmentation Features Feature extraction Dynamic environments Bayesian networks Appearance models Química Operations research & management science Medicina iii Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Interdisciplinar Geociências General engineering Farmacia Engineering, electrical & electronic Engineering (miscellaneous) Engineering (all) Engenharias iv Engenharias iii Engenharias ii Engenharias i Enfermagem Educação Economia Direito Computer science, artificial intelligence Computer science applications Ciências sociais aplicadas i Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência da computação Biotecnología Biodiversidade Astronomia / física Artificial intelligence Arquitetura, urbanismo e design Administração, ciências contábeis e turismo Administração pública e de empresas, ciências contábeis e turismo |
Data: | 2012 |
Autor: | Serratosa, Francesc Alquezar, Rene Amezquita, Nicolas |
Drets: | info:eu-repo/semantics/openAccess |
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