RT info:eu-repo/semantics/article
T1 RSMAT: Robust Simultaneous Modeling and Tracking
A1 Nuevo Chiquero, Jesús
A1 Bergasa Pascual, Luis Miguel
A1 Jiménez Molina, Pedro
K1 Incremental clustering
K1 Appearance modeling
K1 Face tracking
K1 Robust fitting
K1 Real-time
K1 Driver monitoring
K1 Electrónica
K1 Electronics
AB 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.
PB Elsevier
SN 0167-8655
YR 2010
FD 2010-12-01
LK http://hdl.handle.net/10017/43607
UL http://hdl.handle.net/10017/43607
LA eng
NO Ministerio de Ciencia e Innovación
DS MINDS@UW
RD 02-may-2024