Autor según el artículo: Serratosa F; Alquézar R; Amézquita N
Departamento: Enginyeria Informàtica i Matemàtiques
Autor/es de la URV: Serratosa Casanelles, Francesc d'Assís
Palabras clave: 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
Resumen: 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.
Áreas temáticas: 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
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: francesc.serratosa@urv.cat
Identificador del autor: 0000-0001-6112-5913
Fecha de alta del registro: 2023-02-19
Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion
Enlace a la fuente original: https://www.sciencedirect.com/science/article/abs/pii/S0957417412001017
Referencia al articulo segun fuente origial: Expert Systems With Applications. 39 (8): 7302-7318
Referencia de l'ítem segons les normes APA: Serratosa F; Alquézar R; Amézquita N (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
URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
DOI del artículo: 10.1016/j.eswa.2012.01.088
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2012
Tipo de publicación: Journal Publications
Repositori URV