Repositori institucional URV
Español Català English
TÍTULO:
A probabilistic integrated object recognition and tracking framework - imarina:9285158

Autor/es de la URV:Serratosa Casanelles, Francesc d'Assís
Autor según el artículo:Serratosa, Francesc; Alquezar, Rene; Amezquita, Nicolas
Direcció de correo del autor:francesc.serratosa@urv.cat
Identificador del autor:0000-0001-6112-5913
Año de publicación de la revista:2012
Tipo de publicación:Journal Publications
Referencia 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
Referencia al articulo segun fuente origial:Expert Systems With Applications. 39 (8): 7302-7318
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.
DOI del artículo:10.1016/j.eswa.2012.01.088
Enlace a la fuente original:https://www.sciencedirect.com/science/article/abs/pii/S0957417412001017
Versión del articulo depositado:info:eu-repo/semantics/acceptedVersion
Acceso a la licencia de uso:https://creativecommons.org/licenses/by/3.0/es/
Departamento:Enginyeria Informàtica i Matemàtiques
URL Documento de licencia:https://repositori.urv.cat/ca/proteccio-de-dades/
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
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
Entidad:Universitat Rovira i Virgili
Fecha de alta del registro:2024-10-12
Busca tu registro en:

Archivos desponibles
ArchivoDescripciónFormato
DocumentPrincipalDocumentPrincipalapplication/pdf

Información

© 2011 Universitat Rovira i Virgili