Symbiotic trackers’ ensemble with trackers’ re-initialization for face tracking

Victor H. Ayma, Patrick N. Happ, Raul Q. Feitosa, Gilson A.O.P. Costa, Bruno Feijó

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


Visual object tracking aims to deliver accurate estimates about the state of the target in a sequence of images or video frames. Nevertheless, tracking algorithms are sensitive to different kinds of image perturbations that frequently cause tracking failures. Indeed, tracking failures result from the insertion of imprecise target-related data into the trackers’ appearance models, which leads the trackers to lose the target or drift away from it. Here, we propose a tracking fusion approach, which incorporates feedback and re-initialization mechanisms to improve overall tracking performance. Our fusion technique, called SymTE-TR, enhances trackers’ overall performance by updating their appearances models with reliable information of the target’s states, while resets the imprecise trackers. We evaluated our approach on a facial video dataset, which characterizes a particular challenging tracking application under different imaging conditions. The experimental results indicate that our approach contributes to enhancing individual tracker performances by providing stable results across the video sequences and, consequently, contributes to stable overall tracking fusion performances.

Idioma originalInglés
Título de la publicación alojadaInformation Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings
EditoresJuan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas14
ISBN (versión impresa)978-3-030-76227-8
EstadoPublicada - 2021
Publicado de forma externa
Evento7th Annual International Conference on Information Management and Big Data, SIMBig 2020 - Virtual, Online
Duración: 1 oct. 20203 oct. 2020

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen1410 CCIS
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937


Conferencia7th Annual International Conference on Information Management and Big Data, SIMBig 2020
CiudadVirtual, Online

Nota bibliográfica

Funding Information:
This work was supported by CAPES of the Ministry of Education and CNPq of the Ministry of Science, Technology, Innovation and Communication, Brazil.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


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