Symbiotic tracker ensemble with feedback learning

Victor Hugo Ayma Quirita, Patrick N. Happ, Gilson A.O.P. Costa, Raul Q. Feitosa

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)


Visual tracking is a challenging task due to a number of factors, such as occlusions, deformations, illumination variations and abrupt motion changes present in a video sequence. Generally, trackers are robust to some of these factors, but do not achieve satisfactory results when dealing with multiple factors at the same time. More robust results when multiple factors are present can be obtained by combining the results of different trackers. In this paper we propose a multiple tracker fusion method, named Symbiotic Tracker Ensemble with Feedback Learning (SymTE-FL), which combines the results of a set of trackers to produce a unified tracking estimate. The novelty of the method consists in providing feedback to the individual trackers, so that they can correct their own estimates, thus improving overall tracking accuracy. The proposal is validated by experiments conducted upon a publicly available database. The results show that the proposed method delivered in average more accurate tracking estimates than those obtained with individual trackers running independently and with the original approach.
Idioma originalInglés
Título de la publicación alojada2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)
Lugar de publicaciónUnited States
ISBN (versión digital)9781467379625
EstadoPublicada - 7 nov. 2017
Publicado de forma externa
EventoSIBGRAPI Conference on Graphics, Patterns and Images - Niterói, Brasil
Duración: 17 oct. 201720 oct. 2017
Número de conferencia: 30

Serie de la publicación

NombreBrazilian Symposium of Computer Graphic and Image Processing
ISSN (versión impresa)1530-1834
ISSN (versión digital)2377-5416


ConferenciaSIBGRAPI Conference on Graphics, Patterns and Images
Título abreviadoSIBGRAPI
Dirección de internet


Profundice en los temas de investigación de 'Symbiotic tracker ensemble with feedback learning'. En conjunto forman una huella única.

Citar esto