RSMAT: Robust Simultaneous Modeling and Tracking
Identifiers
Permanent link (URI): http://hdl.handle.net/10017/43607DOI: 10.1016/j.patrec.2010.07.016
ISSN: 0167-8655
Publisher
Elsevier
Date
2010-12-01Funders
Ministerio de Ciencia e Innovación
Comunidad de Madrid
Ministerio de Educación y Ciencia
Bibliographic citation
Nuevo, J., Bergasa, L.M. & Jiménez, P. 2010, "RSMAT: Robust Simultaneous Modeling and Tracking", Pattern Recognition Letters, vol. 31, no. 16, pp. 2455-2463
Keywords
Incremental clustering
Appearance modeling
Face tracking
Robust fitting
Real-time
Driver monitoring
Project
Info:eu-repo/grantAgreement/MEC//TRA2005-08529-C02-02
info:eu-repo/grantAgreement/MEC//PSE-370100-2007-2
Info:eu-repo/grantAgreement/MICINN//TRA2008-03600
info:eu-repo/grantAgreement/CAM//S-0505%2FDPI%2F000176
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/submittedVersion
Publisher's version
https://doi.org/10.1016/j.patrec.2010.07.016Rights
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
© 2010 Elsevier
Access rights
info:eu-repo/semantics/openAccess
Abstract
This paper describes a robust on-line appearance modeling and tracking method, based on simultaneous modeling and tracking (SMAT). The appearance model is defined by a series of clusters, built in a video sequence using previously encountered samples. This model is used to search for the corresponding element in the following frames. Three alternative incremental clustering methods are proposed to increase the robustness and description capabilities of the model. The proposal is evaluated on an application of face tracking for driver monitoring. The test set comprises sequences of drivers recorded outdoors and in a truck simulator, which contain examples of occlusions and self-occlusions, as well as illumination changes. The performance is evaluated and compared with that of the original SMAT proposal and the recently presented stacked trimmed active shape model (STASM). Our proposal shows better results than the original SMAT and similar fitting error levels to STASM, with much faster execution times and better robustness to self-occlusions.
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