Open AccessCovariance‐based online validation of video tracking
Author(s) -
SanMiguel J.C.,
Calvo A.
Publication year - 2015
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2014.3405
Subject(s) - tracking (education) , covariance , computer science , ground truth , stability (learning theory) , artificial intelligence , computer vision , state (computer science) , online model , machine learning , eye tracking , pattern recognition (psychology) , data mining , algorithm , mathematics , statistics , psychology , pedagogy
A novel approach is proposed for online evaluation of video tracking without ground‐truth data. The temporal evolution of the covariance features is exploited to detect the stability of the tracker output over time. A model validation strategy performs such detection without learning the failure cases of the tracker under evaluation. Then, the tracker performance is estimated by a finite state machine determining whether the tracker is on‐target (successful) or not (unsuccessful). The experimental results over a heterogeneous dataset show that the proposed approach outperforms related state‐of‐the‐art approaches in terms of performance and computational cost.